{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align = 'center'> Learning To See </h1>\n",
    "\n",
    "<h2 align = 'center'> 第15部分:信息 </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# 启用 pylab 模式，使得可以在 Jupyter Notebook 中实时显示图表\n",
    "%pylab inline\n",
    "\n",
    "# 导入自定义的支持函数库\n",
    "from supportFunctions import *\n",
    "\n",
    "# 导入 pickle 库，并使用别名 cPickle\n",
    "import pickle as cPickle\n",
    "\n",
    "# 定义 pickle 文件的文件名\n",
    "pickleFileName = 'data/fingerDataSet' + '.pickle'\n",
    "\n",
    "# 以二进制读模式打开 pickle 文件，使用 latin1 编码\n",
    "with open(pickleFileName, 'rb') as pickleFile:\n",
    "    # 使用 pickle.load() 从 pickle 文件中加载数据\n",
    "    data = cPickle.load(pickleFile, encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 重新设定只使用3张图像作为训练数据：\n",
    "trainingExampleIndices = [7, 30, 46]\n",
    "\n",
    "# 从数据集中获取指定索引的图像样本\n",
    "trainingExamples = [data[index] for index in trainingExampleIndices]\n",
    "\n",
    "# 从图像样本中提取特征和标签，使用 'image1bit' 作为特征，dist=4 表示样本之间的间隔\n",
    "trainX, trainY = extractExamplesFromList(trainingExamples, whichImage='image1bit', dist=4)\n",
    "\n",
    "# 设置两张图像用于测试：\n",
    "testingExampleIndices = [40, 41]\n",
    "\n",
    "# 从数据集中获取指定索引的测试图像样本\n",
    "testingExamples = [data[index] for index in testingExampleIndices]\n",
    "\n",
    "# 从测试图像样本中提取特征和标签，使用 'image1bit' 作为特征，dist=4 表示样本之间的间隔\n",
    "testX, testY = extractExamplesFromList(testingExamples, whichImage='image1bit', dist=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 让我们使用scikit learn的决策树实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4428,  103],\n",
       "       [ 105,  238]], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入 scikit-learn 中的决策树模块\n",
    "from sklearn import tree\n",
    "\n",
    "# 创建决策树分类器，设置信息熵（entropy）作为划分标准，最大深度为3\n",
    "clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3)\n",
    "\n",
    "# 使用训练数据拟合（训练）决策树模型\n",
    "clf = clf.fit(trainX, trainY)\n",
    "\n",
    "# 使用训练好的模型对测试数据进行预测\n",
    "yHat = clf.predict(testX)\n",
    "\n",
    "# 导入混淆矩阵用于评估分类器性能\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# 计算混淆矩阵，用于比较预测结果和真实标签\n",
    "confusion_matrix(yHat, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RdSIMwxgcHBSRPXv2WPcODg6qvaOjoyLidDqtPRw5ciSZTBpvX6WrsprWLWntA4GAOtzo6KjH49F1PXNqhohEo1Gv1+vxeFRXyWTS4/F4vV5zmkb+yNPk+Q4x4Ut0//33a5pWX19v3ahpWiQS6evrM2eC9PX1RSIRcwaHmszS2dnpdDrVY7fbvXnz5qwzVrIeAgAAVCcSHAAAFENdkO/ZsycajaZSqTVr1hiGsWvXLrVXpQAuv/zyeDyu63ogEMjs4c4771S3BTGvz91ud9oWK5/Pp8pS1NTUqBUZWbMSTz75pLUru93udrtF5MCBA4VEPl0OHz6s63rWlSPHjx83l+SIiK7rL774YmYzc9aGqkJqFhkt5BAAAKAKkeAAAKAYXV1dIuJ2uxsaGlpbWzMnU3R2di5evLiurs7hcHi93swezLuimvLfBtVaRFNlOnp6ejKbqWMtXLjQnCKxcOFCFWqBkVsVXYPj3/7t30TkuuuuS9seCoXcbnfaEpX29nZrzREVnvn6bNiwQbIVGc11CAAAUJ1IcAAAUIza2lrDMGKxmLqJaUNDg8PhMJeBBAIBr9frcrkikUgsFhsfHy9ttFb5I58WiUSip6fH4/FkpmzUupvMu6iY+QtVQMT6RPXYOukj/yEAAEB1IsEBAEDxamtrt27dOjo6GolEdF1XdTTk7Zue7tq1q76+vra2dsGCBVM/1tjYmPl4ZGRE3s4FpHG5XPJ2LY88VTNyRZ6muBocp06dEpHVq1cXfnZm/mLlypVpJ6uqb6jzmsohAABAZSPBAQBAMdRtVg8fPiwiNTU1V155ZWYblYZIpVI+n2/qRwwEAuqyf2xsrLe3V0Suv/76zGaNjY0i4vP5EomE2hKNRm02W3d3d+GRT9Fzzz0nIsuXL8/cpV4KVf5DbVGLU8yXSN0gNhAImA1U9Y0bb7yxwEMAAIDqNK/UAQAAUJZuv/32np6etWvXWjf6/X71IBgMOp3OzMvvkZERaymNybrsssvMxx6PJ2st0vr6enXbFGvhD03TWltbC4l8Whw7dkxEVO2PNK2trU899VRDQ4N1ozW8mpoa9epZ43e5XOZtViY8BAAAqE7M4AAAoBhr1qyJxWLmIhGPxxMOh807ejQ3N5spA4/HMzw8HIvFROSpp54q+ohdXV1qmoO606qqFZqrZTAYNNd0+P3+3bt3mzU780c+LVT108wqqmpjb29vMBhUCQtN04LBYG9vr7Vxc3Pz4OCgil81yLzJS55DAACA6mQr5D72AABUgIGBgU2bNpXjB5+6ZUk5Rl6ObDZbf39/U1PTFPtRa4Wy3sq3rFXqeQEAKgAzOAAAAAAAQNkjwQEAAAAAAMoeCQ4AAAAAAFD2uIsKAABzHdU3AAAAJsQMDgAAAAAAUPaYwQEAADAj9u7dq+6AU2E2btxY6hAAAMiCBAcAAMD0+8pXvqLuqFp5Lr300lKHAABAFiQ4AACYHuq3+rlQL8M6a8AwjDyTCMxoU6nUwMBAe3u7iHg8ntbW1mXLlmXtME8P+/bt6+vr03Vd0zRN0z73uc8tWrSoiPjj8XhdXV3mKxkKhVT/LpfL5XLV1tZa92YNUnWS9oIUEVIR1q5du3bt2tk5FgAAEGpwAABQtTRNMx+3traq7IaIeL3e5cuXx+PxwntIpVKtra1Op1PXdRHRdb29vX3z5s2JRGKyUSUSibq6usztDofD7L+np6euri4UCpl7x8bGJnsgAABQYUhwAABQmQzDULMVjAyxWExEfD6fahkKhXRd9/v9am8kEhGRnp6etK7y9LBv3z7VQzKZNAwjmUx6PB5d13t7eycb9rZt2zI3qgh9Pp/q3zCMYDDodDrT8ho+ny8tzrSXAgAAVDASHAAAVBc1RcLv95uLUPr6+kSkqalJ/VlfXy/vTHAU2ENbW5vdbhcRu93udrtFRP23cN3d3adPn87crvrfvHmz6l9ENmzYICL79+9Xf548eVJEVq1aNanDAQCASkKCAwCAP0qlUjabraOjI217R0eHzWZLpVIiEo/Hu7u7bTabzWZzOBzWVRJWqkH+LdFoVPXscDii0WiewGy5FXGaO3fu1DStra3N3BIOhw3DMHMHahlIMBicbA/WNmZvhYtGo263u6urK3OXCsnap3p87NixyR4FAABUKhIcAAD8kd1u9/l8PT091soRiUSip6fH5/PZ7XZd1+vq6sxZCbquO53OXDmO/Do7OxsaGtQsCV3XGxoaOjs7p+Us8otGo16v96677sq6V+VuHA5HMBhsbm4uogfTyMiI5M2SZLZvaGgIBoNppUMVVexD5ZgU9dicZnL8+HERufjiiwOBgEr9BAIBa3sAAFDxSHAAAPB/PvWpT4mIdT6FeqwusB0Oh4gMDg6qmg6jo6Mi4nQ6J3sUlSPweDzWihVerzdXXc/MEhhpNSYKd//992uaphahZFq1apXP59M0LU/iJn8Ppt7eXk3T1EKSCaVSKbfb7fF4cmVVWlpaRGTfvn1me7P8h1VdXZ1ZKrW9vb21tZUcBwAA1YMEBwAA/6e2tlbTNFXxQenr63O5XKrYhMopXH755fF4XNf1QCBQ3FGefPJJEXG73WkVKw4cODAN55Db4cOHdV23Li1JU19fv3Xr1nA47Pf7nU5n5sKZCXtQOjs7vV5vV1dXgQtVfD6frut33nlnrgYbNmxQaRc1O2PhwoVpDdQLaOaeVBVSXdfNnAgAAKh8eX4UAgCgkvT39xfywafuITI8PGwYxvDwsIhEIhFzr8fjyfVhmutxrr2Ffy4X1z5zu8vlEhHzRiR5JJNJEdE0rYge1EsUi8UmPIqilrFYcxNZ4x8fH/f7/SqqYDCYq5lV2ikU+M1HRPr7+wsMHgAAzB3z8nxnAgCgCn30ox8VkaeeemrZsmWqhqXaIiKBQMDr9bpcrsbGxosvvnjJkiWLFy8uZayToYqJeDyeQmZVqDaqtGfhPSQSiZ07d8bj8eHhYfMGKxNSa3zWrl2btl2VUDXeTkwsWrSora3NnDyibhCbdaGKVdopAACACsYSFQAA3sFut/v9/vb29kQi4XQ6/X6/eT2v6jvs2rWrvr6+trZ2wYIFBfZprVoqInnmQWR9ep5fKgo/r1OnTonI6tWrM3c5HA7zNjHWgFWchfQgIvF4fPPmzSKye/fuwrMbBcqMUN0XdunSpbkaqMdppwAAACoYCQ4AANKtX79eRNTsjM985jNpe9X9QXLVuVRUUdLDhw+rljt37rTubWxsFBGfz2cmPqLRqM1m6+7unsazSPPcc8+JyPLlyzN3qRKeAwMD6s9UKtXb22vGWUgPY2NjdXV1tbW1XV1dixYtmlRgubI21sdpEY6MjOzZs0dE1q1bZ21grbihHqedAgAAqGTTsc4FAIAyUGANDkX98u9yuawbc930VBXssH6wprU0UyFmV5m1PDRNGx8fn5YzzfoRr84o1yFURsbK4/EU3oOqjpHnm8akvnVkNk4mk5kRqkoceRqknUKBMQg1OAAAKE/M4AAAIAv1y//tt99u3djc3GxeyXs8nuHh4VgsJiJPPfVU2tObm5uDwaC65Pb7/Vu3bk1r0NXVFQwGzQUUfr9/9+7dk537MCk9PT0ikusQ4XDYDNjlckUika6ursJ7MG/OOkPsdvvu3butL34sFrPeU9Zut/f29k54CgAAoILZjMks3wUAoHwNDAxs2rSpGj740spzzhE2W4m/dRT4sthstv7+/qamplkJCgAATBtmcAAAgBl3+PDhPMtYAAAApo4EBwAAlclms6k5C3PBwYMHzTu8zr459VIAAIAZQoIDAADMuMwqJAAAANNrXqkDAAAA02yuVd8oOV4QAACqATM4AAAAAABA2SPBAQAAAAAAyh4JDgAAAAAAUPZIcAAAAAAAgLJHkVEAQHVpbGyc6UOcO3fuvPP4CWE2nD179vzzzy91FAAAYE7g6xcAoFpceumlGzdunOmjpFKp/fv3JxKJmT4Q3nzzzf379//iF7+Y3m43btx46aWXTm+fAABgFti4cRoAANPl8ccfb2pquvrqqx955JH3v//9pQ6nwhmG8e1vf/tv//Zvb7rppu9///sLFy4sdUQAAKCUmMEBAMD08Pv9N91004YNGw4cOEB2YxbYbLYtW7Y88cQTTz/99OrVq5999tlSRwQAAEqJBAcAAFN19uzZLVu2uFyur33ta8Fg8F3velepI6oi69evj8ViNTU1n/jEJ3bv3l3qcAAAQMmwRAUAgCl5/fXXW1paHn/88X/9139taWkpdThV6syZM16vt6ur65Zbbunp6bnwwgtLHREAAJhtJDgAACje6dOnNU17+eWXH3nkkWuvvbbU4VQ7Xddvv/32D3zgA3v27LniiitKHQ4AAJhVLFEBAKBIsVhszZo1b7311s9+9jOyG3OBpmmxWGz+/Pkf+chH9u7dW+pwAADArCLBAQAoMVtepY4up//4j/+45pprrrrqqp/+9Kcf+MAHSh0O/qimpua//uu/vvjFLzY1NW3ZsuWtt96arp6n+K90jv97BgCgApDgAABg0nbs2NHU1HTrrbc+9thjdru91OHgHRYsWLBjx44HH3zwgQceaGho+NWvflXqiAAAwGwgwQEAKDHDIteWuePMmTMdHR1bt279+te//r3vfW/+/PmljgjZ3XrrrUePHv3tb39bV1f3xBNPTFe3RjbT1TkAAJgKEhwAABTq1Vdf/fSnP/3v//7vjzzyyFe/+tVSh4MJfOhDHzp8+HB9ff2GDRu2b99+7ty5UkcEAABmEAkOAEAZUPULxsbGHA5HZ2enZKtokLklGo12dHTYbDaHwxGNRqcYw4svvrhu3bqRkZGnnnpK07Qp9obZ8Z73vCcUCn33u9/9p3/6J4fD8corr8zo4eLxeHd3t/qn6HA4QqFQrpbmP06bzdbZ2RmPx3M1mJZ/vQAAVANuEwsAmENUhiLzs0lt93g8Xq83GAw2Nzdntkzb0tnZ6fV6rZ14PJ6urq7iAjt06NDnP//5pUuX6rp+ySWXFNcJSujo0aONjY1nz54dGBhYs2ZNET3k+sdp0nXd4XCkbVT/XNOenrVlJBKpr69Xj6f3Xy8AAFWCGRwAgLKxcuVKwzDU5WJ+0WjU6/V6PJ5kMmkYRjKZVMmRzN/JCxEKhRoaGj75yU8ePHiQ7EaZ+tjHPnb06NGVK1f+xV/8xY4dO4ruJ88tVFTOYnBwUBXmGB0dFRGn05nZiWo5OjqqWg4ODorInj171N7p/dcLAED1IMEBACgb5u/bE3ryySdFxO12q1uc2O12t9stIgcOHJjUEQ3D2L59e0tLS3t7+549ey688MJJhow55OKLL/7Rj370j//4j1u3br3llltef/316e1fZSsuv/zyeDyu63ogEMjVUi1x2rNnTzQaTaVSa9asMQxj165dau90/esFAKDasEQFADCH5F+ikmdBStqWtGIcVoV/8L3xxhtf+tKXBgYGdu7c6XK5CnwW5r4nn3yypaXFbrfv3bv36quvLvBZEy5RkWxLS+Sd/ybV43g8XldXp/ZqmnbXXXdZk3fT8q8XAIAqxAwOAACy+O///u9PfepT+/bte/zxx8luVJjrr7/+6NGj73vf+9auXRsMBqer20Ag4PV6XS5XJBKJxWLj4+O5WtbW1hqGEYvFfD6frusNDQ0Oh4MVKAAATBEzOAAAc0jRMzgSicTixYvNLR0dHT09PclkUk3yn6yhoSFN084///zHHnvsQx/6UDFngjnvzJkzHo/nG9/4xq233vq9733v3e9+d/72E87gSGuQSqUWLlwo2WZwWI2NjZ08ebKhoUGm6V8vAABVixkcAICypKoYHD58WERSqdTOnTutexsbG0XE5/MlEgm1JRqN2my27u7uCXt+/PHHr7322j/90z8dHBwku1HB5s2bd9999z3yyCO6rl977bWnTp2alm5HRkZEJJVK+Xy+XG3U/V/Vv96amporr7zSuncq/3oBAKhmJDgAAGWppaVFRNauXWuz2cRwf9IAACAASURBVBYuXKh+KjfV19erG08sXrxY3eqioaFB07TW1tb83fr9/ptvvvmzn/1sJBJ5//vfP4MngLnhc5/73JEjR86cOfORj3zkkUcemUpXarXL8uXL1b9JsxiHSnlY3X777fL2v16bzXbZZZeJiN/vV3uL/tcLAECVI8EBAChLzc3NwWBQzePw+/1bt25Na9DV1RUMBs3yGX6/f/fu3YsWLcrV4dmzZ++55x6Xy/X3f//3wWDwXe9618wFjznlz/7sz55++ummpqYvfOELW7ZsOXPmTHH9NDc3m0kKj8czPDwci8VE5KmnnkpruWbNmlgs5vF4zMbhcLitrc1sMNl/vQAAQKjBAQCAiLz++ustLS2PP/74Aw88cMstt5Q6HJTGgw8+2NHR8fGPfzwYDC5ZsqTU4QAAgMkhwQEAqHanT592OBxjY2MPP/zwJz/5yVKHg1KKxWKNjY2vvfZaX1+fKvwJAADKBUtUAABVLRaLrVmz5o033vjZz35GdgN1dXXPPPPM+vXrP/vZz27fvv3cuXOljggAABSKBAcAoHo9/PDD11xzzYoVKw4ePPiBD3yg1OFgTviTP/mT/v5+n8/39a9//fOf//yrr75a6ogAAEBBSHAAAKrUjh07Ghsbb7nllh/+8Id2u73U4WAOsdlsW7ZsiUajzzzzzKpVq44cOVLqiAAAwMRIcAAAqs6ZM2e+/OUvb9269etf/7rf758/f36pI8JcdO2118ZiseXLl1933XU7duwodTgAAGACFBkFAFSXV199dePGjUeOHHnooYccDkepw8Fcd/bs2a6urq6urpaWlp6enosuuqjUEQEAgOxIcAAAqsipU6duvvnmVCoVDoc/+tGPljoclI0f/ehHra2tixcv3rt371VXXVXqcAAAQBYsUQEAVItDhw6tXbv2ggsuOHz4MNkNTMqNN94Yi8XsdvuaNWv6+/tLHQ4AAMiCBAcAoCr09/c3NDRcc801hw4duvTSS0sdDsrPpZde+pOf/OTLX/5yc3PzHXfc8eabb5Y6IgAA8A4kOAAAFc4wjO3btzudzvb29r1791544YWljgjlat68effdd9/DDz/c399/zTXXvPTSS6WOCAAA/B8SHACACnH27NnMjW+88UZra+v/+3//7zvf+c6OHTvOO48PPkzVX/7lXz799NNvvPHGxz/+8f/8z/8sdTgAAOCP+J4HAKgEJ0+e3LBhQ9qqgd/+9rc33HDDj370o/3793/5y18uVWyoPMuXL3/66acdDseNN954zz33ZCbXzp07Fw6HSxIbAABViwQHAKAS/M3f/M0TTzzR0dFhbjlx4sTHPvax06dPHzx4sL6+voSxoSK9+93vfuCBB37wgx98+9vfvuGGG8bHx61777vvvr/6q786duxYqcIDAKAKkeAAAJS9xx57bP/+/SLy/e9//5//+Z9F5Iknnrj22muXLFkyODi4YsWKUgeIinXbbbf99Kc/HR0d/djHPnbo0CG18cknn+zs7Dx37lxra+tbb71V2ggBAKge52/fvr3UMQAAULw333zzpptueu211wzDEJEDBw4kEomtW7dqmvbwww8vXLiw1AGiwi1ZsuSLX/zi0aNH77333gULFlx55ZX19fV/+MMfzp0798orr8ybN2/9+vWljhEAgKpgU18HAQAoU/fdd5/H4zGLINhsNhFpa2vr6elRj4FZYBjGfffd19nZuXDhwtdee82cuDFv3rzjx49fffXVpQ0PAIBqQIIDAFDGfvOb31xxxRW///3vrRvPP//89773vceOHbv00ktLFRiqU3Nz88DAgPXL1fz58z/0oQ8dO3Zs3rx5JQwMAIBqQA0OAEAZ27p1a2aNg7Nnz6ZSqU996lOpVKokUaE6PfbYY2nZDRF56623nn/++W9961uligoAgOrBDA4AQLk6dOjQtddem+uD7Pzzz//0pz+t6/r5558/y4GhCo2Ojn74wx9+/fXXz507l7l3/vz58XicercAAMwoZnAAAMrSuXPnOjo6ciUv5s+ff/bs2dOnT584cWKWA0MV+sMf/uBwOP7nf/4na3ZD+dKXvpRnLwAAmDoSHACAsvTAAw8MDQ2dOXPGuvH8888/77zzLrroor/+678+evRoPB7/8Ic/XKoIUT1eeeWVz372s5dccomILFiwILPBW2+9deTIkX/5l3+Z9dAAAKgiLFEBAJSf11577fLLL3/llVfMT7H58+efOXNm9erVX/rSl2699dYLL7ywtBGiOp04cWLPnj0PPfTQyZMnL7jggjfffNO6d8GCBSdOnLjiiitKFR4AAJWNBAcAoPzcfffdO3fuPHv27Lx5886cObN48eI77rjji1/84gc/+MFShwaIvJ3pePjhh5977rn58+erUrjz5s1bu3btU089xQ2MAQCYCSQ4AABl5vnnn//whz+sshsOh6O9vf2GG2447zwWXWIueuGFF/bu3RsKhZ5//nm1JRAIbN68ubRRAQBQkUhwAADKzA033PDrX/+6ra3tlltued/73lfqcICC/OIXv1CZjtHR0aGhIVWwAwAATCMSHACq3S9/+cu777671FGgUG+++ebvfve79773vdPec1NTU2Nj47R3i7I2ODj4zW9+c3r7/N3vfvfmm2/OxL9hlNBXvvKVtWvXljoKAKh2TOgFUO1SqdTevXtfe+21UgeCglxwwQUzcWW4f/9+biiLTC+//PLevXunt8+LLrqI7EaF2bt378svv1zqKAAAMq/UAQDAnPDNb35z5cqVpY4CJXPVVVeVOgTMXXv27Cl1CJjTqBoLAHMEMzgAAAAAAEDZI8EBAAAAAADKHgkOAAAAAABQ9khwAAAAAACAskeCAwAAAAAAlD0SHAAAAAAAoOyR4AAAAAAAAGWPBAcAzKpAIGCz2To7O9O2d3Z22my2QCCg/rRZZHYSj8ezbg+FQg6Hw2azdXR0xONxc3v+3mZIKpUKBAIqHofDEQqFUqlUnva2bGYtWqAsFD2A5Hk/Mj4AACqHAQDVbWhoSESGhoZm7Ygul0tEwuGwuSUcDouIy+Uyt+QZqMfHx7Nu1zQtbYQPBoMT9jZz1GlaaZqWq/Ho6GhpP6RWrFixbdu2WTscykV/f/9c+7JU3ACS5/3I+DB1ItLf31/qKAAABjM4AGC23XvvvSLicDhGRkZEZGRkxOFwmNut1EidtnHbtm2ZfYZCIV3XfT5fMplUzwoGg06nc2xsLFc/Myoej/f09Hg8ntHRUcMwRkdHXS6XruvqlHPx+Xxpn1KzFjBQLooYQPK/HxkfAAAVgwQHAMy2mpqaSCQiIm63O5VKud1uEYlEIjU1NRM+t7u7+/Tp05nb+/r6RGTz5s12u11t2bBhg4js37+/6Djj8Xh3d3dxzz1y5IiItLa2qpOqqalRP9geO3Ysa/uTJ0+KyKpVq4qMFagaRQwgk30/FoLxAQAwB5HgAIASqK+v93g8uq4PDAzouu7xeOrr6yd8VjQadbvdXV1dmbt0XRcRM7thPi7uAubw4cMdHR11dXXq2klyLIDPswxezRxZvHixuWXJkiUicuLEiSLiAWA12QFket+PjA8AgDmLBAcAlIbb7dY0rb29XdM08zohj5GRkYaGhmAwWFtbm7lXFeCwVulTj3t6egoPKZVK6brucDjWrl0rIuFw2Kz3MVler1femXBZtGiRuT3T8ePHReTiiy9WNRRVucT8RQeBajapAWSy78esGB8AAHMfCQ4AKA273b5+/XoRWb9+vfWLflZqIrrH42lubs7aoKWlRUT27dtntvf5fIUHMzY2FgqFFi5cGAgEWlpaRkdHd+3apWmauuqQvMX8Cj/KhOrq6trb29Xj9vb21tZWrmGArCY1gEwR4wMAoFyQ4ACA0ojH4+p3V7fbbb2la1Y+n0/X9TvvvDNXgw0bNmia5nQ61c+bCxcunFQwl112mdPpDAaD4XC4ubm5kGog00u9FIODg+Z1UTAY1HXdTNkAsJrUADJFjA8AgHJBggMASiCVSnV2dorI8PCwiHR2dub5KTIUCnm93sHBQfP30kx2u3337t1+v19ENE0LBoNZS3XkMjo6qu664nA4QqGQWiFvNdk19pOlLlrWrFljblFzVVTxVABWkxpApo7xAQBQLkhwAEAJqBkZPp9v2bJl5uNcjZ1Op4isXbs27Zoh7fph0aJFbW1thmGoX1nVRUiBC1Vqamqam5uTyWRbW1tfX99ll13W0dGh63oikSjuBD0ej2SrCaK2F04VTwVgNakBRKb8fmR8AACUCxIcADDbotGo1+vVNG3r1q0isnXrVk3TvF5vNBotuk+Hw2Gz2awXDOrGikuXLi28E7vdrmlaOBweHBxUfZq3OZjsGvuVK1eKiLUG4UsvvSQiuSa3Z8avHqubRwIwFTGATPb9mBXjAwBg7iPBAQCzamxsrKGhQd45t0I9bmhoyJz7LdkuHqzb1WNVZHRgYED9OTIysmfPHhFZt25dEUGuWbNm165dsVhsUpVKrVasWCEivb296ozGxsb27t0rIqtXr87aPq1Iqvm4sbGxuACAilTEACKTfz/mx/gAAJi78iTdAaAaDA0NicjQ0NDsHE795BgOh9O2h8NhEXG5XOrP/EN05t5kMqnuFGsVDAbzP2tGZcZjnl1mPFnj93g8sxbtihUrtm3bNmuHQ7no7++fU1+Wih5AJvV+nAXlNT5MSET6+/tLHQUAwJhDn9kAUBKzmeBQRUBzfS9X68/9fr8x+QSHYRjj4+Oqf3WIWCxWyLNmjopHXZZomub3+5PJZJ54kslkMBhU7V0uVyQSmbVQDRIcyGFOJTimMoBM9v0408prfJgQCQ4AmCNsxrTeohwAys6JEyeuvvrqoaEhtSx8jlDVQ6d3iJ6JPivGVVdd1dTUtH379lIHgrllYGBg06ZNZfeuKeLNzvgwFTabrb+/v6mpqdSBAEC1owYHAAAAAAAoe/NKHQAAIKfp+k3VejdZAFWiwAGE8QEAUDGYwQEAAAAAAMoeMzgAYC6a3pXwrKsHqsqk3vKMDwCAisEMDgAAAAAAUPZIcAAAAAAAgLJHggMAAAAAAJQ9EhwAgClJpVKBQMDhcNhsNofDEQqFUqnUVNqnUqlQKGQ2CAQCiUQiT4MJjwhg7pjUiGHLLWv7eDyeuWvCIQUAUDkMAKhuQ0NDIjI0NFSqAHw+X1mPxi6XK+2TRdO0otsnk0lN0zIbjI+Pqwbj4+P5GxRnxYoV27Ztm0oPqEj9/f1z7e1ZVSNGrq+vWZ8yPj6e+eV2wiFlWohIf3//NHYIACgOMzgAoMTcbnepQyhePB7v6enxeDyjo6OGYYyOjrpcLl3XR0ZGimu/b98+Xdf9fn8ymTQMI5lMejweXdd7e3tVg0cffVTX9WAwaH6SBYNBXdcfffTRWTpnoKSqasTI/OYai8VERGV50mzbti1z44RDCgCgkpDgAAAU78iRIyLS2tpaU1MjIjU1Nern2WPHjhXXvq+vT0Ta2trsdruI2O12dTlnXtS1t7eLSHNzs9mneqy2A5jLJjtipEkkEnV1dX6/f9myZWm7uru7T58+nfmUCYcUAEAlIcEBANMjGo12dHSoNd7RaNS6S60YTyQS3d3d5rJzc5e1jflgbGzM4XB0dnaanaRVncjsX7VRj63L2h0OR0dHh7V9KpWy2WxpG61dFb7ifWxsTEQWL15sblmyZImInDhxorj24XDYeOe8dHVZYsqcbZ5/OzA3MWIo+UeMNDt37tQ0ra2tLW17NBp1u91dXV2ZT5lwSAEAVJTZWAcDAHPYtNTg8Hg8aaOrx+Mx96otaVfgapFF5phsPt1sU2D/4XDY2sBcox6JREQkFouZ7QcHB+Xt7/1pJvt5kXXXNLY3DGN4eNj6UqjTTFuikut0CkcNDmQ1QzU4GDEm3JhJBRaJRNK2W4eIQrpKG1KmhVCDAwDmBmZwAMBURaNRr9fr8Xisa7y9Xm88Hrc2q62tVQ3U13Q1cdp45zdys/HKlSsNw1CLL8z+zVXrqv+0X30DgYC1ga7rqkF9fb2maXv37jVbHjx4UESWL1+eeS55PjCm6dWatN7eXk3TNmzYoP7UNC0SifT19Zk/FPf19UUiEWZwoFwwYhTn/vvv1zStvr7eujGVSrndbo/HY122ll/akAIAqChTyY4AQAWY+gwO9WOpuhRRksmkiPh8PvWnGm+tRfutI3DaaJzZWK1RV5ciyujoqIi4XC7rU4aHh3M1UBdIZg8y0Y1OCpf1oyTP58tk26vX1vpjsvH2XSSs/H5/sWfwR8zgQFYzMYODEaOQjWlyzSJRL6Z5+hN2lXVImTphBgcAzA0kOABUu6knOCZMImd+557wciVX40J6yLpR0zR1+aTuQZArIzDhueQ/Sp6NRbTPeimiFqRkLlGZ4oRzEhzIaiYSHIwYhWxMo7I21qyQ8fZ7f3BwsMCuZii7YZDgAIA5gyUqAFAV2tra3G53IpF44YUXRGT16tXT0q26YDDLE5qPM0sATKp9IpHo7OyMx+PDw8O1tbXWXU6nU7LdRUVN4AcwLebIiKEkEgl1c9m0+qBqNFi7dm1aZdPMKqd5hhQAQCUhwQEAU5X1p0VlGvtXdx9QRkZGzO2mzAbWa4brrrtORKLRqEoE5PqKnycjnrX9ypUrRWR8fNzc8tJLL4mIugdkce3j8fjmzZtFZPfu3Zk3g8xF1/UCWwKlxYhR+IihnDp1SqaQZCluSAEAlCMSHAAwVY2NjSLi8/kSiYTaEo1GbTZbd3d34Z1Yf9LM2n8gEFAXJGNjY729vSJy4403WptlNrj++uvNvXa7PRgMOp1OXdfz/1g6KStWrBCR3t5e89CqNmGuS5EJ24+NjdXV1dXW1nZ1dS1atCizB1WAIxqNmq+YugVmZmEOYG5ixCh8xFCee+45yVblNFdWxfp4wiEFAFBR8q1fAYAqMEO3idU0LU/dO+sW8/YfqsJf1sG5kJs+prWxNlBUIUN555L1qcu8fYlZqjDzZCds7/f7839gjY+PZ/ZgfbWLQw0OZDVrt4llxMh6soqae1LIezzzuRMOKdNCqMEBAHMDCQ4A1W5aEhyGYQSDQXMGuN/vz3UHhMwtsVhMPVHdpyDXN+9gMKguDDRNSyuoaT5FzWJQN1LNGqRqOcVcQJrx8XG/32/G5vf70+bep51R/va5LkWsPSSTybRXI+ts/0khwYGsZijBYTBiFDxiZN2SS67nkuAAgCphM2bgRuUAUEZOnDhx9dVXDw0NqcXh5UiV05twPB8ZGVm+fLnL5dq1a9esxFVOrrrqqqampu3bt5c6EMwtAwMDmzZtqrAvS4wY085ms/X39zc1NZU6EACodtTgAIBqoZbZpxUaBICsGDEAAGVnXqkDAADMOPOOiR6Ph1skAsiPEQMAUKaYwQEAlU8td/f5fF1dXaWOBcBcx4gBAChTzOAAgLI34Vr6cDg8O5EAmPsYMQAAlYoZHAAAAAAAoOyR4AAAAAAAAGWPBAcAAAAAACh7JDgAoELYbDbz3gclZ8tmUg1EJBQKORwOm83W0dERj8fT9qZSqUAgoBo4HI5QKJRKpWb2rIAKMqdGjFQqZb7fHQ5HIBBIJBKFN8g6nlhPcMIGAIDKQJFRAMA0Gxsbm2IDEXE4HLquq8c9PT09PT3BYLC5udlscM899/T09KjHuq7ruq5pGsURgbKTSqVaW1vN97v+tt27dy9atKiQBrmo28FMpQEAoLwwgwMAMCN8Pp/xToU3CIVCuq77fL5kMql2BYNBp9NpZkbi8XhPT4/H4xkdHTUMY3R01OVy6bo+MjIye2cIYDrs27dP13W/36/e78lk0uPx6Lre29tbYAMjQywWExGfz1dgAwBAZSDBAQCYZidPnhSRVatWFd2gr69PRDZv3my329WWDRs2iMj+/fvVn0eOHBGR1tbWmpoaEampqXG5XCJy7NixaToJALNEvd/b2trU+91ut7vdbhFR/y2kQZpEIlFXV+f3+5ctW1ZcAwBAmSLBAQBTEo1GOzo61Fruzs7OtFIR8Xi8u7tb7VV1Isxd5vJvXdfVXnMCdigUUnuztrfuzV91wozN4XBEo9FJRW41+8vX1UthZjfMx2b+Qk3lWLx4sdlgyZIlInLixIkZCgmYFowYmcLhcNoML+t7v5AGaXbu3KlpWltbW9ENAADlKnPOHgBUlaGhIREZGhoq4rlZKz5EIpE8e4PBoNqr/kxrE4vFPB5P4e01TTODSRvV0/oREY/HU2DkaXJ/hmT/EFGzvmOxmN/vV83MueUFNlAL461bksmk9YhZj54npAmtWLFi27ZtxT0XFay/v7/of1SZGDEKfKGGh4et5zKpBpFIJE9shTQogoj09/dPY4cAgOIwgwMAiudwOERElYEwDGNwcFBE9uzZY907ODio9o6OjoqI0+m09nDkyBF1Ga++c9fV1cnbF/ZqS1r7QCBgVp1Qq9Azf2gVkWg06vV6PR6Pdcm61+s1f3TNH3maPJ8ieV6curq69vZ29bi9vb21tTXt1+M8DVpaWkRk37596s9UKsVSeVQARowCX6je3l5N09TCtMk2uP/++zVNq6+vz/XcCRsAAMrYZDMiAFBhpjKDQ0008Pl8kUjEOt3Aanx8PBaLhcNh8wdStV09Hh8fN1uqLdZ+MtsPDw+be9X1j8vlymysjpU5A8Ks61lI5EVTkZjXaYZhBINByfhtOU+DZDKZdneDrK9e1uMWFzMzOJDV9M7gYMQohAomFosV0UBlXtSSlqwmbFAcYQYHAMwNJDgAVLupJDhUHX5F07TMOc+Zs77zXKLn3zLhJX3m46wKjDzzKHl6K4S8c3r8hA3Gx8fVAhZN01TiY1KvxmSR4EBW05vgYMSY8CWaSnbDMAxVbDhPCmbCBsUREhwAMDewRAUAildbW2sYRiwW8/l8uq43NDQ4HA5zUncgEPB6vS6XKxKJxGKx8fHx0kZrlT/yGWLWRCykwaJFi9ra2gzDCIfDzc3NqqqouVBFXeRY17yox1mvD4E5ghEjj0QioWqXDg8P19bWFtdA3T06VwnSCRsAAModCQ4AmKra2tqtW7eOjo5GIhFd19WqeBFRBSZ27dpVX19fW1u7YMGCqR9LXecrIyMjkuOSPs+vlIVEniZPmjxre4fDYbPZMrMPKqriGqg7yy5dulT9uXLlShGxXgG+9NJLIqLuGgvMZYwYmeLx+ObNm0Vk9+7dWW/dOmEDETl16pSIrF69OtdRJmwAACh3JDgAoHjqpomHDx8WkZqamiuvvDKzjbqomK4ymYFAQF2xjI2N9fb2isj111+f2ayxsVFEfD5fIpFQW6LRqM1m6+7uLjzyoqWVCDUfq6gKbzAwMKD+HBkZUeUM161bp7asWLFCRHp7e81XY+/evcKlC+Y2RoysxsbG6urqamtru7q6Fi1aVEQD5bnnnhOR5cuXF90AAFD2JrGcBQAq0VRqcKh6dWn8fr/aqwpnZlJl/zIH4fxb1OO0X1+t93FMe3rm77SappkVCvNHPkWZJULTQi2uQdotITMbmNUTi0ANDmQ1vTU4GDGyMm8XnanABoqah2Ktw5pmwgZFE2pwAMDcQIIDQLWbSoLDMIxYLGZeGHg8nrTi/Ob3co/HMzw8rAr1qauC4i5XDMNQv+tm1vnLfHowGDQXffj9/rSv9fkjn6JkMhkMBlUOQtUUmGwDs8ioCi+zpqBqoHrQNM3v90+lcCAJDmQ1vQkOgxEjm1zJCym4BmquM8p6oOkKO61nEhwAMBfYjILvSQ4AFenEiRNXX3310NCQKuswZ9lsNhFh0J4hV111VVNT0/bt20sdCOaWgYGBTZs2leP7jhFjNtlstv7+/qamplIHAgDVjhocAAAAAACg7JHgAAAAAAAAZY8EBwAAAAAAKHvzSh0AAKAgrKUHUDhGDABAFWIGBwAAAAAAKHskOAAAAAAAQNkjwQEAAAAAAMoeCQ4AmFk2m81ms5U6CpG3I7HGY8sm11MyG2Tdm+d84/F40S+Fruu5njsyMtLZ2amOGwgEEolErvMF5pq58+8zz/sl67uvwLd/KBRyOBw2m62joyMej6d1kkqlzAYOhyMUCqVSqeLizxxephghAwgAlCMSHABQpcbGxqbYIBdN0zI3JhKJurq64jqMx+MOhyPXruXLl3u9XvVne3v75s2bi75GApAmz7svK+vb3+FwOJ1OXddFpKenp66uLhQKmXsTiURra6vZQNd1p9PZ2tpq5igLN6nhpfAIAQBlhwQHAFQXwzCst1fw+XzGO6W1z9PAyBCLxdRTMo+7bdu24gI+fPhwrkuXVCpVV1enadro6KhhGMlk0ufz6bq+b9++zDMFMKG0d02ed9+Eb/9QKKTrus/nSyaTqkEwGHQ6nWbm9NFHH9V1PRgMmj0Eg0Fd1x999NHJhp11eJlihAwgAFCOSHAAQJU6efKkiKxataroBmnUj6h+v3/ZsmVpu7q7u0+fPl1EkN3d3WvXrg0Gg1n3vvDCCyLS0tJSU1MjIna7ffPmzSLS19dXxLEAWOV/96XJfPurt+HmzZvtdrvasmHDBhHZv3+/+rO9vV1EmpubzU7UY7V9UnEWMrwUESEAoOyQ4ACACaRSKbU8O217R0eHzWZTqyHi8Xh3d7dara1WkmftKnM5d+aWaDSqenY4HNFoNE9gBa4wnzU7d+7UNK2trS1tezQadbvdXV1dRfTpdrvD4bD1Esjq4MGDIrJu3Tpzi91uNwwjHA4XcSygCBU8PuR/96XJfPurdR9m7sB8fOzYMfVn1rVsebZnVfjwUkSEAIDykzl/DwCqytDQkIgMDQ3laaOmNI+Pj5tbxsfH5e3lG1kvp81519bBNnPgTdvi8XjS+vF4PLmimuzYnrZLnVQsFvP7/WqX3+83p2oX0sAqEomISCQSSds+PDxsvhpT+dzJ+lx1IWQYRjAYVI99Pp/1f1PhhOYLNAAAIABJREFUB12xYsW2bduKiw0VrL+/f8J/PxU5PhS4S8n69ldvSeuIkUwmrV2pVyZtiYqIhMPhPMeyKnx4KS7CCbu1Nuvv7y8wbADAzCHBAaDaFZLgUCu3M7+IDw8PG29/Ax4cHFS7RkdHc1205L+AUV/BPR6P+sKdTCbV9UwsFpuWM007etZKGZqmmV/3J2xgpWmapmlpG5PJpKZp5jVYgZcKhQRv3Zh21ZcWYYEHJcGBrApJcFTk+FDgLiXr21+9CObLYgZs7SoSiVjna2ialpkkzWVSw0vRERY4gAgJDgCYG1iiAgATq62t1TTNWtmhr6/P5XKptdxqPL388svj8biu64FAoLijPPnkkyLidrvVNGm73e52u0XkwIED03AOGVTn5nWX8XaFP1Whs5AGpsOHD+u6nrk4RZX8vPPOO2cifivzx/NcEQIzpyLHh8Llevtv2LBB0zSn06nWxSxcuDDzucePH1frRBRd11988cUCj1v48DKVCAEAZWa2MikAMEcVMoPDePvnU/WTrJoXbf2lMXPquEz+F9rJjtXFtc9/miKS+TvnhA1cLpe8c6a38favo9b8yFQ+d7I+V23MnGFujbDAgzKDA1kVMoPDqPTxIf+bKOvbXxkfH1cL3DRNy1xIkjaBIuuWXCY1vBQdYf5urYQZHAAwN5DgAFDtCkxwqCtnv99vvP3d2vy6rL4fu1yuSCQSi8XU8vvyuoApvE1mA3W+mbUA8oRXyAVDIYGp68b8LQs8HAkOZFVggqOyx4c8u3K9/bNSy3PMO09n7VYmyrFan1vICU4lwlxBZo2HBAcAzAUsUQGAgtjtdr/f397enkgknE6n3+83a++rmxru2rWrvr6+trZ2wYIFBfaZSCSsf+b5mTHr0/MM7oUc3eFwmHd5UNRjFUYhDZRTp06JyOrVqws56PRauXKliIyNjZlbskYIzLTKGx8KlOftnzmAqDtPL126NH+f1kUrczNCAMCcRYIDAAq1fv16EVm8eLGIfOYzn0nbOzIyIiKpVCprbU5FldM7fPiwarlz507r3sbGRhHx+XzmhU00GrXZbN3d3dN4FqaWlhYRsZarUI9VGIU0UJ577jkRWb58eVr/uS6rpvESS90gNhAImJcoKsIbb7xxWvoHCldh40OBcr395e0BZGBgQP05MjKyZ88esdzXWb0U0WjUfP+qG+jmeYlMhQ8vU4kQAFB+8iT4AaAaFLhERVE/orpcLutGNSM9k/UeCllbmt/jza4y1+prmpZ239OipR1L3YMg7XDWidwTNrC+JoUEmfm5U/gnUa6WmS9+2v+dAg/BEhVkVeASFaWSxocCd+V5+2cdQKz1NcbHxzMbWM+o8PEhT+OpRFh4DMISFQCYG0hwAKh2k0pwqFKC1sp2ilpmLyIej2d4eFjdNlItyE/7fhwMBtVX6qx7VQNzhYXf75+uq5esx0omk2Y8qkZA2lMmbJC128IDmMpzTYODg+oVM8sEFnEIEhzIalIJjgobH6a4y7CU8FTnnnlHW+sIo96/RdzjOX/jKUZIggMAyovNmNalmABQdk6cOHH11VcPDQ2pgg4VzGazichcG/Zttpn9JCrwrK+66qqmpqbt27fPXCQoRwMDA5s2bZpr75qZUJ3jQyEBSAEvi81m6+/vb2pqmpWgAAA5UYMDAFAyhw8fNn8+BQArxgcAwGSR4ACA6mKz2dRvknPBwYMH29raZqjzOXWmQFmYU++aGR0fJjSnXgoAQIFIcAAASmbr1q2lDgHAHMX4AACYrHmlDgAAMEvm2ur6mVZt5wtMBe+XNLwgAFCOmMEBAAAAAADKHgkOAAAAAABQ9khwAAAAAFPyxhtvlDoEAAAJDgAAAGBqOjs7NU07fvx4qQMBgKpGkVEAEBG5+uqrSx0CgDmK24ViQq2trfv27fvoRz960003eb3e2traUkcEANXIRo1oAFXutdde27Zt20MPPbR06dJ777231OGUxqZNm+666661a9eWOpBSWrly5cqVK0sdBeaWX/7yl4cOHSp1FHPLt771LRG5++67Sx3I3LJu3bqlS5c+9thj27Zti8fjX/jCF7q6upYvX17quACgupDgAFDVjh8/ftddd/3kJz/ZuHFjd3f3pZdeWuqISsNms/X39zc1NZU6EABznRooBgYGSh3IHGUYxmOPPfYP//APzz777Be+8AWv17ts2bJSBwUA1YIaHACq1G9/+9stW7Z8/OMf/9///d+f/vSnAwMDVZvdAABMF5vNpmnaM888EwqFnn322RUrVjQ1NZ08ebLUcQFAVSDBAaDqvPXWWzt27Ljiiiv27t373e9+9/Dhw+vWrSt1UACAynHeeec1NjY+//zzoVAoHo9fddVVt91224svvljquACgwpHgAFBdDhw4sGrVqr/7u7+7/fbbf/7zn7e3t593HiMhAGD6qTTHCy+88NBDDw0ODq5YseK22247depUqeMCgIrF13oA1WJkZOTmm2++4YYbPvjBD/785z/fsWPHe97znlIHBQCocOZsjt27dx86dGjFihV33HHH6dOnSx0XAFQgEhwAKl8ymbznnnv+/M///PTp0z/+8Y91Xf/gBz9Y6qAAAFVk/vz5t9122wsvvBAIBA4cOHD55Zffcccdv/rVr0odFwBUFBIcACrZuXPnHnzwweXLlwcCgW984xtHjx5dv359qYMCAFQpM82xc+fOH/7whyrN8Zvf/KbUcQFAhSDBAaBi/fjHP/7IRz6yefPm5ubmF198ccuWLeeff36pgwIAVLsLLrigvb391KlT3/72tx977LErr7xyy5Yt4+PjpY4LAMoeCQ4AFejll1++7bbbrr/++ve///3Hjx/fsWPHwoULSx0UAAD/x0xzfPOb39yzZ8+VV155zz33vPLKK6WOCwDKGAkOABXl97///fbt25ctW/b000/ruv7EE0+sXLmy1EEBAJDdggUL2tvbf/GLX3i93h/84AeXXXbZPffc8+qrr5Y6LgAoSyQ4AFQIwzD27NmzYsWKHTt2bN++/dlnn7355ptLHRQAABO76KKLtmzZ8uKLL3o8nkAgoNIcyWSy1HEBQJkhwQGgEhw9evSTn/xkc3Pz+vXrf/7zn3/1q19dsGBBqYMCAGASLrrooq9+9aujo6Nf+9rX/H7/FVdcsX379lQqVeq4AKBskOAAUN5+/etf33HHHZ/4xCfmzZv3zDPPPPjgg4sX/3/27j0uinr/H/hnuHjtiFlpnspMK0XUwFtipmVe0lo8JaBgEB5RITtZyknTJfCSaUFJaupiqdCRi9pJ1iKz3cxLeA8SREg7gmlBZax5B9nfH59v85tm9jK7O7ufvbyef/hYZz/72fcsn8vse2c+04l1UAAAAHa65ZZbaJrj1VdfzcrKommOixcvso4LAMADIMEBAJ7qxo0bWVlZPXv2/OyzzzZs2PDVV1+FhoayDgoAAEABf/vb3+bOnXv69OkXX3xxxYoV3bt3X758+ZUrV1jHBQDg1pDgAACPpNVqg4OD58+f/8orr1RXV8fHx3McxzooAAAAJXXo0CE9Pf306dMzZ85cunRp165dly9ffvXqVdZxAQC4KSQ4AMDDnDx5cuzYsePHj+/fv/+JEyfS09Nbt27NOigAAABnue2222ia45///OeiRYtomuPatWus4wIAcDtIcACAx7hw4cKsWbP69Onzyy+/7Nmzp7Cw8N5772UdFAAAgCvcfvvty5YtO3PmzJQpUxYuXPjggw9mZWUhzQEAIIQEBwB4gKamJo1G06NHjy1btqxevfrQoUNDhw5lHRQAAICr3XHHHTTNERsbO2/evB49emRlZV2/fp11XAAAbgEJDgBwdzqdLiws7MUXX4yNjT158uT06dP9/DB2AQCA7+rYseOyZcuqq6v/8Y9/0DSHRqNpampiHRcAAGP4kgAA7uv777+Pjo4eOXJk165dKysrs7Ky2rVrxzooAAAAt3DPPfdkZWVVVVWNHz/+pZdeeuCBB5DmAAAfhwQHALijy5cvp6en9+nT5/jx45999plWq+3evTvroAAAANxOly5daJpj9OjRM2fOfPDBBzUazc2bN1nHBQDAABIcAOBempubc3JyunfvvnLlyuXLlx8/fnzs2LGsgwIAAHBr995777p1677//vtRo0bNnDmzT58+OTk5SHMAgK9BggMA3MihQ4ceeeSRqVOnjh8//uTJk7NmzQoICGAdFAAAgGfo2rXrunXrqqurH3300X/+858PPfRQTk5Oc3Mz67gAAFwECQ4AcAvnzp2Lj48fPHhwmzZtjh07tm7dujvuuIN1UAAAAJ7nvvvuW7du3fHjx/v160fTHFu2bDEajazjAgBwOiQ4AICxK1euLF++vGfPniUlJQUFBTqdrk+fPqyDAgAA8GzBwcE5OTllZWVhYWGTJk0KDQ1FmgMAvB4SHADAklarDQkJWbx48Zw5c8rLy6OiolhHBAAA4D1CQkJomqNHjx4TJ04MDw/XarWsgwIAcBYkOACAjWPHjg0bNmz8+PGPPvroqVOn0tPTW7ZsyTooAAAAL9S7d+/CwsLS0tIuXbqMHz9+yJAhSHMAgFdCggMAXO23336bNWvWoEGDrl+//s033+Tk5Nx5552sgwIAAPByffv2LSwsLCkpue222yIiIh555BGdTsc6KAAAJSHBAQCu09jYmJWV1b17923btn344YcHDhwYPHgw66AAAAB8yMMPP6zVar/55psOHTqMHDly6NCher2edVAAAMpAggMAXOTLL78MDQ197bXXkpKSTp48GR8fz3Ec66AAAAB8EV2MY//+/a1atXriiSeGDh369ddfsw4KAMBRHNZSBgBnq6qqmj179mefffb000+vXLmya9eurCMC8vzzzx87doz/b3l5+T333BMUFET/GxgY+Omnn3bu3JlRdADgRnJzczMzM2/evEn/e/bsWULIPffcQ//r7+8/Z86cuLg4ZvGBw/bt25eWlqbX60eOHPnGG28MGjSIdUQAAHZCggMAnOj3339fvnz5u+++GxISkpWV9eijj7KOCP7P0qVLFyxYYO7Z3r17Hz9+3JXxAIDb+v777x988EELBaqrqx944AGXxQNOsm/fvtTU1N27d48cOfLNN98cMGAA64gAAGyGS1QAwCmam5tzcnJ69Oixfv36t9566/Dhw8huuJXJkyebu0QoMDDw+eefd3E8AOC2HnjggdDQUJMjBsdxoaGhyG54h6FDh3711Ve7du26ePHiwIEDR40adfToUdZBAQDYBgkOAFDeV199FRYWlpiYGBMTc/r06VmzZvn7+7MOCv7i3nvv7d+/v8lvLE1NTVFRUa4PCQDcVnx8vMlh3N/fH/lQLzNy5MiDBw/u2rWroaFh4MCBKpXq22+/ZR0UAIBcSHAAgG0sX9d29uzZ+Pj4ESNGdOzYsbS0NCsri1/WAdyNyW8sfn5+4eHh9957L5OQAMA9TZo0qbm5Wbr95s2byId6pZEjRx4+fPiLL7746aef+vfvr1KpysrKWAcFAGAdEhwAYIMLFy6MHj36ypUr0qcuX76cnp7+4IMPHjx4cMeOHbt27erVq5frIwT5Jk2aJE1X+fn5xcfHM4kHANxW586dhw4d6uf3l+NGPz+/oUOH3nXXXayiAmejaY7t27efO3euX79+0dHRVVVVrIMCALAECQ4AkOv69esqlerLL79csWKFcLvRaNyyZUtwcHBWVlZ6evrx48efeuopVkGCfHfcccfw4cNFJ3EYjcYJEyawCgkA3Jb0PikcxyEf6vU4jlOpVEePHv3kk0+qq6t79eoVHR1dXV3NOi4AANOQ4AAAWYxG45QpUw4dOkQIWbJkyfnz5+n2w4cPDx06dNKkSY899lhVVdXcuXNbtGjBNFKwQVxcnPAkDn9//5EjR95+++0MQwIA9zRhwgRRPpTjuGeffZZVPOBKNM1x7Nix/Pz87777Ljg4ODo6+tSpU6zjAgAQQ4IDAGRJT08vKChoamoihDQ1Nc2dO/f8+fMzZswYPHhwy5Ytv/3225ycnI4dO7IOE2zz7LPPBgQE8P81Go3SH2kBAAght95665gxY/gRIyAg4Mknn+zQoQPbqMCV/Pz8oqKiTpw4kZ+fX1ZW1qtXr/j4+NOnT1t4yfnz5xsbG10WIQAAEhwAYF1+fv7ixYv5FeYaGxs/+uij7t27f/nll1u2bNHr9X379mUbIdinXbt2Tz31FP+NJTAwcPz48WxDAgC3NXny5Js3b9LHN2/efO6559jGA0zQNEdlZeV//vOfkpKS4ODg+Pj4//3vfyYLz549OyYmhm82AADOxlm+IQIAwN69e5944gnRLzB+fn4dO3Y8ffp0mzZtWAUGiti2bVtUVJTRaAwICHj22WcLCgpYRwQAburKlSu333771atXCSGtWrX69ddf27ZtyzooYKmxsTEvL2/RokVnz55NSEh4/fXXhYvOVlRU9OnThxASGxubk5MjWqQWAMAZMNAAgCWnT59WqVTS316am5vr6+u3bt3KJCpQ0FNPPUWzVDdv3pw8eTLrcADAfbVp0+aZZ54JDAwMDAx89tlnkd2AwMDA+Pj4ysrK7OzsXbt2devWbcaMGfwqXWlpaQEBAUajMT8/f+rUqfhVFQBcAGdwAIBZv/3224ABA3788Ue69IYIx3G33nrr//73v3bt2rk+NlBQQkLCpk2b/va3v/3yyy8tW7ZkHQ4AuK9PP/306aefJoTs2LEDN8wCoRs3bmzcuHHRokW///57YmLiM888M2LECP6Lhp+f38yZM9977z22QQKA18MZHABg2rVr18aNG3fu3DmT2Q1CiNFovHDhwvLly10cGCguNjaWEBIVFYXsBgBYNnr06Hbt2gUFBY0ePZp1LOBeWrRoMX369O+///6NN94oKCgYNWqU8LY7zc3Nq1evTk1NZRghAPiCAOtFAMD30LtpHDt2TJrd4DiuRYsWN27cMBqNbdq0OXTo0LVr11q1asUkTlDEiBEjOnXqRNMcAAAWBAYG0rEiMDCQdSzgjlq3bv3yyy8//PDDQ4YMET3V3Nz8xhtvtG7dev78+UxiAwBfgAQHAJiwYMECfn0NjuP8/f1ppuP222/v379///79w8LCwsLCunXrxnEc00hBAQEBAbNmzXrsscdYBwIAHiAmJgYjP1i2ZMmSwMBA6Q1ijUbjggULAgMD//3vfzMJDAC8HtbgABvggMYXREZGPvnkk4mJiYQQjuO6du06cODAfv36hYWFhYaGduzYkXWALlVRUdG7d2/WUYCS0tLS0tPTWUcBHqxXr16VlZWsowDGgoODT5w4wToKN3X06NGBAwda+IrBcdz777+flJRE/4s+BQR9CpSDMzjANi+//HJ4eDjrKMBZ3n333cuXLx87duz9998PDQ3t27cvFsknhGRmZt59992sowAFzJ49m3UI4A0iIyOjoqJYRwHMbNmypaKignUU7svqFShGo/GFF15o27ZtXFwc3YI+5ePQp0BBSHCAbcLDw6Ojo1lHAc6yZcsWQsjq1atZB+JexowZExISwjoKUADO3QBFhISEYCr0ZSdOnMCXMXMuXLjQqlWrBx54oLa29tq1a4QQPz+/wMDApqYm4S3njUZjQkJC69atIyMjCfqUz0OfAgUhwQEAAAAAAAro0KHD9u3b6eO6urofBKqqqk6fPv3LL7/Qq1eam5tjYmKwSDkAKAsJDgAAAAAAUFinTp06deokurT5+vXr//vf//isR25u7o0bN1hFCADeBwkOAAAAAABwhZYtW/bs2bNnz578ll69ejGMBwC8jB/rAAAAAAAAAAAAHIUEBwAAAAAAAAB4PCQ4AAAAAAAAAMDjIcEBAAAAAAAAAB4PCQ5wC9nZ2RzHpaamiranpqZyHJednU3/ywnQLQaDgb6Wvry6ulpaeX5+fkREBMdxycnJZWVl/HZpbS5AA6bxRERE5OfnGwwGC+U5U1wWLTiD3a3damOw0B3Q2gE8EYYLczBcgFKc0cvQiQBYMgLIRggpKChwUuVJSUmEkKKiIn5LUVERISQpKUkYgKjpqlQqUZM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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入 StringIO 模块用于创建内存中的文件\n",
    "from six import StringIO\n",
    "\n",
    "# 从 IPython.display 模块中导入 Image 类\n",
    "from IPython.display import Image  \n",
    "\n",
    "# 导入 pydot 模块\n",
    "import pydot\n",
    "from sklearn import tree\n",
    "import os\n",
    "\n",
    "# 创建一个 StringIO 对象，用于将决策树导出为Graphviz格式的数据\n",
    "dot_data = StringIO()\n",
    "\n",
    "# 使用 tree.export_graphviz 将决策树导出为Graphviz格式的数据，并保存到 dot_data 中\n",
    "tree.export_graphviz(clf, out_file=dot_data) \n",
    "\n",
    "# 从 dot_data 中读取Graphviz格式的数据，创建图形对象列表\n",
    "graphs = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "\n",
    "# 选择第一个图形对象\n",
    "graph = graphs[0]\n",
    "\n",
    "# 指定Graphviz的可执行文件路径，替换为您的实际路径\n",
    "graph.set_prog(\"C:/Program Files/Graphviz/bin/dot.exe\")\n",
    "\n",
    "# 使用 Image 类将图形对象显示为PNG格式图像\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义lambda函数，根据指定规则判断样本\n",
    "treeRule1 = lambda X: np.logical_and(np.logical_and(X[:, 40] == 1, X[:, 0] == 0), X[:, 53] == 0)\n",
    "\n",
    "# 创建一个包含81个零的NumPy数组\n",
    "ruleVector = np.zeros(81)\n",
    "\n",
    "# 在第40、0和53个元素分别赋值为1、-1和-1\n",
    "ruleVector[40] = 1\n",
    "ruleVector[0] = -1\n",
    "ruleVector[53] = -1\n",
    "\n",
    "# 创建一个6x6大小的图表对象\n",
    "fig = plt.figure(0, (6, 6))\n",
    "\n",
    "# 将规则向量重塑为9x9的矩阵，并以图像形式显示\n",
    "plt.imshow(ruleVector.reshape(9, 9), interpolation='none', cmap='RdBu')\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 330  165]\n",
      " [  79 7293]]\n",
      "Recall (TPR) = 0.667 (Portion of fingers that we \"caught\")\n",
      "Precision (PPV) = 0.807(Portion of predicted finger pixels that were actually finger pixels)\n",
      "Accuracy = 0.969\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为14x8的图表对象\n",
    "fig = plt.figure(0, (14, 8))\n",
    "\n",
    "# 调用自定义函数 testLogicalRules 进行逻辑规则测试\n",
    "testLogicalRules(trainingExampleIndices, data, fig, trainX, trainY, treeRule1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 238  103]\n",
      " [ 105 4428]]\n",
      "Recall (TPR) = 0.698 (Portion of fingers that we \"caught\")\n",
      "Precision (PPV) = 0.694(Portion of predicted finger pixels that were actually finger pixels)\n",
      "Accuracy = 0.957\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为14x8的图表对象\n",
    "fig = plt.figure(0, (14, 8))\n",
    "\n",
    "# 调用自定义函数 testLogicalRules 进行逻辑规则测试\n",
    "testLogicalRules(testingExampleIndices, data, fig, testX, testY, treeRule1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 只是为了好玩，基尼系数也一样吗?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4428,  103],\n",
       "       [ 105,  238]], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入 scikit-learn 中的决策树模块\n",
    "from sklearn import tree\n",
    "\n",
    "# 创建决策树分类器，设置基尼系数（gini）作为划分标准，最大深度为3\n",
    "clf = tree.DecisionTreeClassifier(criterion='gini', max_depth=3)\n",
    "\n",
    "# 使用训练数据拟合（训练）决策树模型\n",
    "clf = clf.fit(trainX, trainY)\n",
    "\n",
    "# 使用训练好的模型对测试数据进行预测\n",
    "yHat = clf.predict(testX)\n",
    "\n",
    "# 导入混淆矩阵用于评估分类器性能\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# 计算混淆矩阵，用于比较预测结果和真实标签\n",
    "confusion_matrix(yHat, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HS7dvpnuOZ7Wvd3d3h9/ryBtFp9q/ned4hkG+AWL0uwyD3xihNJg/XzF/no30mDdAKfZU4HK5bD8Gq+9Ne3u7li1bVvTvZaGbNWuWamtr1dTU5HQoRSXffT0XfdLq/qX0j7epqUnt7e3as2dPLsMCCtbAwIBmz56t/v7+8FyVSK2Qx1FO52ErMnkfGQ8UL/JNZgo531jhdG4i3wBZG2DOZABFr6enR36/3+kwAPwf+iQAOIs8DKAQkZuA0kAxGYCkw9/Qmt/SFpvdu3drzZo1tm2vmN8LIJV8/H3b3SetoP8CcEKh5R4n87AVhfa+AcWgmPsNY0SgNFBMBlD0br31VqdDABCBPgkAziIPAyhE5CagNEx2OgAAzir2ObdygfcEpahc/q7L5TgBFAZyTnZ4/4D00V+yw/sH2IcrkwEAAAAAAAAAKVFMBgAAAAAAAACkRDEZAAAAAAAAAJAScyajpPz+979Xe3u702GgTHV3dzsdQtkYGBigr8NWAwMDTocAFITHH3+c/oCi8PbbbzsdArJEvkGxIN8A0Sgmo6T09PRo2bJlTocBIMd27NihHTt2OB0GSkxlZaXTIQCOu/XWW50OAUCZIN8AQHFyGdzSEtCePXtUU1OjN954Qz/+8Y/1sY99zOmQHOFyubRt2zbV1tY6HQpQMvbv36+5c+fq1FNP1aOPPup0OHnV1dWlxYsXq729XTU1NU6HA6AM9fX16WMf+5juvfdefelLX3I6nIJQU1Ojnp4e9fb26sQTT3Q6HAA24nxuotdee02XX365RkZGdN999+nqq692OiSg2A0wZzLK3g9/+ENdcMEFOu644/Tcc8+VbSEZQG7cc8892rdvn+655x6nQ8m7RYsWafXq1Vq3bp3GxsacDgdAmTlw4ICuvfZaffSjH5XH43E6nILx0EMPacqUKVq1apW4rghAqTvllFP03HPPad26dbrmmmv0uc99Tn/84x+dDgsoahSTUbb+/ve/6+abb9YXvvAFrVixQs8884xOO+00p8MCUEJ+97vfacOGDfrGN76hGTNmOB2OI3w+n4499lh9+ctfdjoUAGXm29/+tvr7+/Xwww/riCM47TG9733v07Zt29TZ2amWlhanwwGAnDv66KN111136YknntBzzz2n2bNn63//93+dDgsoWoyqUJaGh4f1iU98Qo888oja29v14IMP6sgjj3Q6LAAl5qabbtIHPvABffWrX3U6FMccf/zxevjhh9XW1qb//u//djocAGVicHBQd955p+644w7mQ49j3rx5uvPOO3X77bfrF7/4hdPhAEBeLFq0SC+++KKuvPJKVVdX67rrrtNf/vIXp8MCig7FZJSdjo4OzZ07V/v379evf/1rLV261OmQAJSgxx57TD/60Y9033336eijj3Y6HEctXrxY//qv/6rrr79eb775ptPhAChxhw4d0urVq3XWWWfpK1/5itPhFKzbbrtNS5Ys0fLly8nNAMpGRUWFHnzwQW3btk07duzQueeeq927dzsdFlBUKCajbBw4cEBNTU266qqr9JnPfEbPPPOMzjzzTKfDAlCC/va3v+n666/X8uXLtWTJEqfDKQj33HOPJk+ezHQXAHLuu9/9rnp6evTwww9rypQpTodTsFwulx5++GEdPHhQV199NfMnAygrS5cu1cDAgM466yx98pOf1Pr167V//36nwwKKAsVklIXXXntNn/zkJ/Wtb31Lra2t2rx5s4499linwwJQopqbm/XGG2/I5/M5HUrBqKio0KZNm7Rlyxb96Ec/cjocACVqeHhY3/jGN3T77bf///buPT6K8t4f+GdJorSlRmsRBS9tPUUjeBLFolFaFe/VjVITbiLq4ZaIleMxorVBOAdt8bjBUuS2UauC5oK1krSmiokiYqICbiTBklptIp42AX9NKIpC4Pn9QWfZ3czMzszuzDOz+3m/Xvvixe7M7Hd2P/vku8/OzuLcc8+VXY7rDR48GM899xzq6+uxbNky2eUQETnqxBNPxO9//3ssW7YMS5cuxZgxY9De3i67LCLX42QypbzXXnsN5513Hnbv3o23334b06ZNk10SEaWwP//5zygvL8fChQsxbNgw2eW4yrXXXoubb74Zt99+O/7xj3/ILoeIUtCsWbMwbNgw3H///bJL8YyLL74YZWVlKC0txdatW2WXQ0TkKJ/Ph5kzZ+Ldd99FX18f8vLysGTJEn5bg0gHJ5MpZQkh8PDDD+Pyyy/HZZddhs2bN+Pss8+WXRYRpbiSkhIMHz4cs2fPll2KK/3617/GgAEDcPfdd8suhYhSzJNPPon169fj8ccfT/tz1Zv1wAMP4Ic//CEmTJiAPXv2yC6HiMhxZ511FpqbmzF37lzcfffduOaaa/B///d/sssiciVOJlNK2r17N6655hrMnz8fixcvxpo1azBo0CDZZRFRinv22Wfx2muvYdWqVcjMzJRdjisde+yxWLFiBZ566inU19fLLoeIUsTf//53lJaWYs6cObjoootkl+M5AwYMwJo1a7B3717MmDFDdjlERFJkZWVhwYIF2LhxI/7yl78gLy8P69atk10WketwMplSzsaNG5Gbm4sPPvgAGzZswJw5c2SXRERpYM+ePZg7dy6mT5+O/Px82eW4mt/vx8SJEzF9+nSe7oKIkmL27NnIzs7G//zP/8guxbOGDBmCZ599Fs8//zyeeOIJ2eUQEUmTn5+PLVu2YNy4cbjhhhswdepU7N27V3ZZRK7ByWRKGUIILFmyBJdddhlGjRqFUCiE888/X3ZZRJQmfv7zn+Orr77CQw89JLsUT1i6dCkOHjyIuXPnyi6FiDyupqYGv/vd7xAMBvlNtASNHTsWc+fOxR133IGWlhbZ5RARSXPMMcdg1apV+O1vf4v6+nqcffbZ2Lhxo+yyiFyBk8mUEvbs2YOioiKUlpbi/vvvx4svvojjjjtOdllElCa2bNmCFStWIBAI4Nvf/rbscjzh+OOPRzAYxBNPPIGXX35ZdjlE5FGfffYZ7rzzTsyYMQNXXHGF7HJSwsKFCzFq1ChMnjwZX3zxhexyiIik+slPfoLW1lacffbZuPTSS3Hfffdh//79sssikoqTyeR5W7duxTnnnIPm5ma8/vrrWLBgAQYMYLSJyBmHDh3CHXfcgfz8fNxyyy2yy/GUgoICFBYWYtasWfjnP/8puxwi8qA5c+YgIyMDDz/8sOxSUkZmZiaqqqrQ1dWFO++8U3Y5RETSDRkyBOvWrcPy5cvx2GOP4Qc/+AG2bdsmuywiaTjjRp72zDPPYMyYMTjttNOwefNm/uAKETlu1apV2Lx5Mx577DH4fD7Z5XjO8uXLsW/fPp7ugohMe+mll/Dss89i+fLlOPbYY2WXk1JOPvlkPP3003jyySexevVq2eUQEUnn8/kwc+ZMvP/++/jmN7+J0aNH4+GHH8ahQ4dkl0bkOE4mkyft3bsXkydPxq233oo777wT69evx4knnii7LCJKM11dXfj5z3+Ou+66C7m5ubLL8aRvf/vb+PWvf41Vq1Zh/fr1ssshIo/Ys2cPiouLMXnyZFx//fWyy0lJ1157Le68807cfvvt+NOf/iS7HCIiV/je974X/kb0vHnzcPXVV+PTTz+VXRaRoziZTJ7zpz/9Cfn5+Vi/fj3q6+uxaNEiZGRkyC6LiNLQPffcg0GDBuGBBx6QXYqnTZgwAT/5yU8wc+ZMnu6CiAyZO3cu9u3bh0cffVR2KSntkUcewciRIzF+/Hjs27dPdjlERK6QmZmJe++9F2+++SY6OjowcuRIPPvss7LLInIMJ5PJU9asWYPzzjsPX//617F582ZcddVVsksiojS1ceNGrFmzBkuWLMGgQYNkl+N5K1aswOeff477779fdilE5HIbNmxAMBjEY489hhNOOEF2OSktKysLa9asQWdnJ+655x7Z5RARucro0aMRCoUwdepU3HzzzRg/fjz+8Y9/yC6LyHacTCZP+PLLLzFnzhxMnToV06ZNw5tvvonTTjtNdllElKb6+vowe/ZsXHnllRg3bpzsclLC4MGD8eijj2L58uXYsGGD7HKIyKW++OILTJ8+Hddeey0mTJggu5y0cPrpp6OiogLLli1DZWWl7HKIiFzla1/7GpYsWYL6+nps2rQJeXl5eP3112WXRWQrTiaT63V0dOBHP/oRnnrqKdTU1GDJkiXIysqSXRYRpbFAIIAPP/wQy5cvl11KSrnppptw/fXX47bbbsPevXtll0NELjRv3jzs2rULK1askF1KWikqKsKsWbNw++234+OPP5ZdDhGR61x11VUIhUI499xzMXbsWMyZMwdfffWV7LKIbMHJZHK1devWIS8vD319fdi6dSsKCwtll0REaa6zsxMPPvgg7r//fnzve9+TXU7KWbZsGXp7ezFv3jzZpRCRy7zzzjtYsmQJysvLcfLJJ8suJ+386le/wne+8x1MmDAB+/fvl10OEZHrDB48GL/73e/w1FNP4Te/+Q1GjRqFUCgkuyyipONkMrlSX18f7rvvPowbNw5+vx+bNm3C6aefLrssIiL89Kc/xdChQ1FaWiq7lJR00kknYfHixfj1r3+NjRs3yi6HiFxi//79mDZtGn70ox/hP/7jP2SXk5YGDhyImpoa/OlPf8LPf/5z2eUQEbnW1KlT8f777+P4449Hfn4+Hn74YRw6dEh2WURJw8lkcp2dO3fi4osvxrJly7BmzRo888wz+NrXvia7LCIi1NfXo7a2FitWrMDAgQNll5OybrnlFvj9fkyfPh379u2TXQ4RucBDDz2Ejz76CBUVFfD5fLLLSVvf//73EQwGUV5ejnXr1skuh4jItb7zne+gsbERCxYswAMPPIArrrgCn3zyieyyiJKCk8nkKo2NjTjvvPPw//7f/0NTUxMmT54suyQiIgDAvn37MHv2bEyePBmXXXaZ7HJS3vLly7Fr1y488MADskshIsm2bduGRYsW4Ze//CW/qeYCEydOxNSpU3Hbbbeho6NDdjlERK6VkZGBe++9F5s3b8bu3btx9tlnIxgMyi6LKGGcTCZXOHjwIBYsWIArrrgCV1xxBTZv3oyRI0fKLouIKGzhwoX47LPP8Mgjj8guJS0MHToUjzzyCBYvXow333xTdjlEJMnBgwcxbdo0nHvuuZg9e7bscuhfli1bhhNPPBE333wz+vr6ZJdDRORqZ599Nt5++20UFxejpKQE48ePx2effSa7LCLLOJlM0u3atQs//vGPsWjRIixevBirV6/GN77xDdllERGFtbe3Y/HixXjwwQcxdOhQ2eWkjWnTpuHKK6/E9OnT8eWXX8ouh4gkKC8vx/vvv48nnngCGRkZssuhf/nGN76BmpoabNmyBf/93/8tuxwiItcbOHAgFi1ahJdffhlvvfUWRo4ciZdeekl2WUSWcDKZpHrjjTeQl5eHHTt24I033sCcOXNkl0RE1E9JSQnOOOMMlJSUyC4l7Tz++OPo6uriZAVRGmpvbw+fa/Kss86SXQ7FGDlyJBYvXoxf/OIXWL9+vexyiIg84fLLL0draysuu+wyXHfddZg1axa++OIL2WURmcLJZJJCCIElS5bg8ssvx3nnnYf33nsPo0ePll0WEVE/q1evxuuvv45Vq1YhMzNTdjlpZ9iwYVi0aBEeeeQRvPvuu7LLISKHHDp0CDNmzMDw4cNxzz33yC6HNMyaNQsTJ07ElClT8Le//U12OUREnnDsscdizZo1qK6uxtq1a3Heeedh69atsssiMoyTyeS43t5eFBYWorS0FAsXLsSLL76I4447TnZZRET97NmzB/feey9mzpyJCy64QHY5aWvmzJm47LLLcMstt/B0F0RpYuXKlXjrrbfwxBNPICsrS3Y5pGPVqlU49thjMXnyZBw8eFB2OUREnlFUVIRQKIQhQ4bg/PPPx4IFCziOkidwMpkctWXLFpx77rl455138Prrr+Pee++Fz+eTXRYRkaqf/exn2L9/Px588EHZpaQ1n8+HYDCInTt34qGHHpJdDhHZrLOzE/fddx/mzp2LUaNGyS6H4hg0aBCee+45NDU14Ze//KXscoiIPOXUU09FY2MjAoEAFi1ahDFjxuDDDz+UXRaRLk4mk2OCwSAuvPBCfOc738HmzZtx0UUXyS6JiAgHDx7Eyy+/3O/6LVu2YNWqVVi8eDGOP/54CZVRpNNOOw2//OUvsWjRImzevLnf7Z999hl2794toTIisur9999XvX7WrFkYOnQo5s2b53BFZNWoUaOwaNEizJ8/H42NjarL7N+/3+GqiIi8wefzYc6cOdiyZQu+/PJLnHvuuQgGg7LLItLEyWSy3T//+U9MmjQJxcXFuOuuu7B+/XoMGTJEdllp74c//CF8Pl/UBQAmTJgQdV1GRgY+/fRTydUS2eeDDz7A1VdfjXHjxmHnzp0ADp+rc/bs2cjPz8fNN98suUJS3H777bjkkkswbdq0qEmJ3/72t8jJyUFtba3E6ojIrGuvvRY//vGPw2MvADz99NN45ZVX8Pjjj2PgwIESqyOz5syZg4KCAkydOrXfh3tVVVW45JJL5BRGlKL4fi71jBgxAk1NTbj99ttRUlKCG2+8kQdLkCtxMpkSEu98Ph988AEuuOACNDQ04I9//CMWLVqEAQMYOzeYNGlS3FOM+Hw+5OfnY9iwYQ5VReS8t99+GxkZGfjDH/6A4cOHo7y8HMuWLcOWLVuwbNkynorHRZTTXXz00Uf4xS9+gV27dqGoqAhFRUXYvXu35tFwROQ+HR0d2LlzJ1555RWceeaZCAaD6O7uRmlpKe644w6MGTNGdolkks/nw5NPPomsrCxMnToVQgjs27cPM2bMwKRJk9DU1MSvbhMlEd/PpaaBAwdi0aJFeOONNxAKhTBixAjU1dXJLosoCmf1yLK//vWvuPLKK/HVV1+p3v7MM8/gBz/4AY4//niEQiFceeWVDldIeoqKiuJO7A8YMAC33HKLQxURydHc3IwBAwbgwIED2LdvH+bOnYv//M//xI033oh///d/l10exfjud7+LBx98EA8++CBOP/10rFu3DkIICCHwyiuvyC6PiAzasGEDBgwYgIMHD+Lzzz9HSUkJRowYgaOPPprnRvew4447DtXV1Xj11Vdx3333YdSoUfjNb34DAMjKysKLL74ouUKi1MH3c6ntoosuwtatW3HDDTeEv/Wxd+9ezeXfeOMNfmBHjuFkMlkihMCtt96KxsZG3H333VG3ffnll5gzZw5uvfVWTJs2DQ0NDRg6dKikSknL4MGDMXbsWGRkZOgu95Of/MShiojk2LhxIw4cOBD+/6FDhwAA1dXVmDJlCrq7u2WVRiq6urrw+uuv4+DBg9i7d2/Uc7dr1y420UQesWHDhqge5NChQ+jp6UF3dzeWLVvGX7P3sNGjR2PcuHF45JFH8OGHH4afy76+Pjz//POSqyNKHXw/l/qys7OxatUq1NTU4KWXXkJubi42bdrUb7ne3l5MmjQJRUVFUb0xkV04mUyWVFRUYOPGjQCAZcuWobq6GgDw5z//GRdccAGeeuoprF27FkuWLEFWVpbMUknHlClTIIRQvS0jIwNXX301f3iMUtrevXvx5z//ud/1yoRyTU0Nhg8fjscffzx8Hcmzdu1anHnmmfjDH/4AAP3Gr8zMTLz22msySiMik9avX9/vDW9fXx8OHDiA+++/H+eccw7ee+89SdWRVXv37sWUKVNQU1MDAFHPsRAC77zzDv7+97/LKo8o5fD9XHooKipCa2srzjzzTFxyySW47777osbXO+64A7t27cK2bdv447XkCE4mk2mffvop7r777vDEis/nw2233YbHHnsMo0ePRkZGBt577z3ceOONkiuleMaNG6c52S+EwJQpUxyuiMhZmzdv1p0kPnDgAPbs2YPZs2fzfLwSffXVV7jhhhswfvx49Pb26h5xweeJyP0+/fRTfPLJJ5q3Hzp0CNu3b8f555+PZ5991sHKKBEtLS3Izc0NTySrTXANGDAAL730ktOlEaUsvp9LHyeeeCJ+//vfY9myZVi6dCnGjBmD9vZ2vPDCC1izZg0OHDiAgwcP4n//93/x6quvyi6XUhwnk8m02267Leo8yUIIHDhwAHfeeScmTJiAt956C9/73vckVkhGffOb38R1112n2oAcddRRuO666yRUReSc5uZm3W9PZGVlITs7G6+88gouv/xyByujSEcffTSmT5+OY445Rvf56uvrY/NM5AHK+ZL1HDp0CBMnTsS4ceMcqooS8emnn+LSSy/FRx99FPcr1r/97W8dqooo9fH9XHrx+XyYOXMmNm/ejL6+PuTm5uKmm26K+iFGn8+HiRMnoqurS2KllOo4mUym/OY3v8Grr76q+rXEjIwM9Pb24uijj5ZUHVlx0003oa+vL+q6zMxM3HDDDRg0aJCkqoic0dTUpHlezqysLAwfPhyhUAgXX3yxw5VRrOuuuw6tra3Izc3VPTfg7t27sWPHDgcrIyKzNmzYgMzMTNXbMjIykJWVhUcffRTPPPMMvv71rztcHVkxbNgwbNmyBRdddJHuBwUHDx7Eq6++is8//9zB6ohSG9/PpZ+cnBw0Nzdj2LBh+Oqrr6K+CXLo0CHs2bMHU6dO1TwFClGiOJlMhv3tb3/DnDlzNAekvr4+VFdX48knn3S4MkrEtdde26/JOHjwIG666SZJFRE556233lI9zcWAAQNw/fXX45133sFpp50moTJSc8opp+Ctt95CaWkpfD5f1FEYCp43mcj9Xn31Vezfv7/f9VlZWRg8eDA2btyIOXPmSKiMEvHd734Xb7zxBlasWIGjjz5a8wODAwcO4JVXXnG4OqLUxfdz6WnFihX4+OOPVednDhw4gPXr12P58uUSKqN0wMlkMmz69On48ssvdZcRQqCkpAQtLS0OVUWJOuqoo1BYWIijjjoqfN2gQYNw5ZVXSqyKyH6dnZ3YvXt31HXKBOW8efNQU1PDI+JcKDMzE4sWLcILL7yAb3zjG/2+1imEQENDg6TqiCie7u5ufPzxx/2uHzBgAMaOHYu2tjacf/75EiqjZBgwYABmzpyJrVu3YuTIkarfJMnIyMCLL74ooTqi1MT3c+nngw8+wD333KP72y9CCNx1112cmyFbcDKZDHn22WdRX18f9xxomZmZ2L9/P2699VbNr46T+0yePDl8hFBWVhYmTZoU1YwQpaK333476sjWzMxMfO1rX8MLL7yABQsWqB71Su5xww03YOvWrRg+fHjU0W8HDx5EQ0MDv9ZH5FIbNmyI+r/yId4999yDl156Cd/61rckVUbJdNZZZ+Hdd9/FQw89hMzMzKhxuq+vDy+++GK/r+UTkXV8P5c+Dhw4gEmTJhkaQ4UQmDBhAvbt2+dAZZROOJlMce3atQt33HGH5u3KUWFHH300rrrqKjz99NPYuHGj7jktyV0uvfRSHH/88QCO/HEiSnVvv/12ePzKysrC0KFD8c477+CGG26QXBkZ9f3vfx+bN29GcXExAIQ/APjHP/6B7du3yyyNiDRs2LAhauzNzs5GfX09Fi1aFPdH+chbMjMzce+992Lr1q0488wzo94b7NmzB2+++abE6ohSC9/PpY+2tjYcd9xx4TFV70ODvr4+/OUvf8Hdd9/tVHmUJtixUVyzZs3C559/HnWUl/Ije4MGDcKECRNQW1uL3t5e/P73v8fUqVN5on+PycjIwJQpUwAAgwcPxg9/+EPJFRHZb9OmTdi/fz8yMjIwZswYxZ4kvAAAIABJREFUhEIhjBgxQnZZZNLAgQOxdOlSPP300zjqqKOQmZkJn8/H8yYTuZRyvuSMjAzk5uZi27ZtuOqqq2SXRTY6++yzsXnzZtxzzz0YMGAAMjMzkZGRgXXr1skujShl8P1c+sjLy8Nrr72Gnp4erF+/HnfddRdGjhwJAOHxNVJfXx9WrlzJMZeSyif4PVDSsXbtWowfPx7A4aNHDhw4gG9961sYP348brzxRlxyySWaP65B3vL222/jggsuwH/913+hvLxcdjlEturr68OgQYPw1VdfYc6cOQgEAhzLUkBbWxuuv/56/OUvf8G4cePwwgsvyC6JiCJ89tlnGDx4MIQQ+OlPf4pAIMCvYaeZpqYm3HTTTfj4448xbNgw7Ny5U3ZJRCmD7+fS20cffYQ//vGPeOmll9DY2Ih9+/Zh4MCB+PLLL+Hz+XDMMcegra0Nw4YNk10qeV8bJ5NJ0+7du5GTk4Pdu3djyJAhmDBhAm688UaMGTOGX0NMUcOHD0dlZSVGjRoluxQiW7333nvIz8/HypUrceutt8ouh5Jo7969mDFjBtavX4/u7m7+vSJykRdffBFTpkzBE088gQkTJsguhyT54osv8LOf/QyPPfYYtm7ditzcXNklEaUMvp8jANi/fz82bdqEl19+GXV1dfjggw8ghMAll1yChoYG9seUqP6TyTt37sRbb70lqyBykaqqKhw8eBDnn38+Tj/9dMd/jOrCCy/EySefbMu2m5qa8Mknn9iybS9744038KMf/Uh2Ga6kHKFvB+bRee+++y6ys7MxfPhw2aVYYmceX375ZfT29tq2faesX78eI0aMwNChQ2WXQv+SnZ1t2+kM2L96w6uvvoqcnJyUPirKzv61pqbGlu3Ksn37duzevZu9p8eccsopyM/Pt2Xb7IkTx/dzyWFXr93W1oa2tjZbtq2np6cHLS0taGlpwciRIzF27FjHayDvuvrqq3HMMcdEXtUGEaO6uloA4IUX6Zfq6urYeCZNYWGh9P3jxVsXOzGPvJi92CknJ0f6/vGSmpecnBzbcsv+lRe3XOzsX2XvGy+8ABCFhYW2ZZw9MS9uudhl/vz50veNF17MXlpbW2Oj3Kp5gkjBs1+QRE4cBV1YWIi1a9fafj/kbTU1NY58FZd5JCOcyuP8+fOxYMEC2++H0seCBQscOaqS/SvJ5ET/Wl1dbeu3U4j0FBUV2X4f7IlJJid67ZycHGzfvt3W+yBKhra2tvCPO8biiVKIiIiIiIiIiIiIKC5OJhMRERERERERERFRXJxMJiIiIiIiIiIiIqK4OJlMRERERERERERERHFxMpmIiIiIiIiIiIiI4uJkMhERERERERERERHFxclkIiIiIiIiIiIiIoor7SaTfT4ffD6f7etY0dvbi4qKChQUFMDn86GgoABVVVXo7e11ZH1yltVcpWoeq6qqwsuWlJSgpaUl7n3U1dU58likA+YxetnIPBYUFKCiogLd3d1Ryyn7rneh5Er1nMZqaWnRrNtoTs0uS85J9Twr6yv1zps3D+3t7XHXi/e3nXn2DmZcnV7G2Vt4CzMevayZvsTKa4fcI12yn8z3jmaXdT0Ro7q6WqhcnTIAmN4/K+tYUVxcHL6vyIvf73dkfTcBIKqrq23bfmFhoSgsLLRt+0ZYzVUq5tHv96suW1lZqbn9UCjkyGPhxJjIPMbnVB57eno08+j3+0VXV1d4WbVl7B57nchjTk6OmD9/vq33YVWq5zRSV1eXZt1mcmpmWTvNnz9f5OTk2LZ9L/avqZ5nrdyFQiHNdeL9bXdLnrXY3b/avf1kY8b7i5dxGb2FGXb3rG7oic1gxg8zOzZbee04xe5+wu5+yCmpnn073ju6vYdR09raKgCI1tbWfjel3WSyWymNRVlZmejo6BBCCNHR0REO8Y4dO2xd323sbpa91qg4zck8VlZWCgAiEAiInp6eftcr60dqamqKGnztlC6TyW4mI4/BYDCcx56eHlFWVhbOqdF67Rh3030y2c2S/Xc4somNZSanych0MnAy2VsSzXNk7hQNDQ0CgCguLlZdx8jfdrfkWQsnk73DrRmPV6/s93ScTPYOJzNutS+Jt10ZOJnsfW597+j2HkYNJ5M9IBgMqgZbCbLeEZrJWN9tOJksl5N5VD6di5xIFuLwwBrbaAghRCAQCG+Dk8npQUYeYyl5jJcF5WjS2NwmCyeT3SuZf4cDgUDUkQuxzOQ00UwnCyeTvSXRPGv9bdfKnNG/7W7JsxZOJnuHWzOuxu7ewgxOJnuHkxm30pcYfe04jZPJ3ufW945u72HU6E0mp9Q5kyPPPaKcdyf2nCxa/+/u7kZ5eXnU+VC01lGT6PmtOjs7AQBDhgyJuv6kk04CALS1tdm6PiWXXVlUW0+Nl/JYV1cHAMjOzo5aVvn/1q1bo64vLS1FbW0tJk6cqFsDHcE8Gl+/trYWQoh+24jNp5alS5fC7/djxowZhpanI9I9p4rGxkaUlpZi4cKFmsuYyWmimSZr0j3PSu4ic6b8va+srOy3vNG/7cyzezDj9mRcDXsLOZhx4xm30pcYfe2Q89I9+3a9d0y5HiZ2etmrR3Yoh4ZrXRRa/1c7d4nyiUPsOmr07tvM+mZvS9b6bgMPH5lsZxbV1lPjpTzGOzJZ776cyLbXj0xmHpMzPu7YsSPuJ9nKV/QaGhribs+qVD0ymTk9LDZnZsc4Izm1smwypNORycxzNOWITKN5s/K33ek8awHS48hkZjyanRl3orcwI12OTGbGo5nNuCLe2Gx1u3ZK9yOTmX3n3jtaWdZpKX9kcmNjIx588EGUlZWho6MDQgh0dHSguLjY8DZyc3PR09MDIQQaGhoAAM8995zh9YUQcS+U+tyQRcBbeZw8eTIAoL6+Pnxdb28vAoGArJJSBvOYPKtXr4bf78c111yjucyvfvUr+P1+jB071sHKvI85Pay3txelpaUoKyuz/M0LIzm1siwZxzz3d8455yAQCMDv92PSpEn9jlJKBubZOcx4f3ZmnL2F85jx/qxmPN7Y7MTfBzKO2U+etOjJY6eX3XRkh1HKpyexP9SlzPBH7o/W/2N/OTFyudh17KB3H0buP9H13Qbw5pHJdmdR7f92cDKPWr9qGvmpqJX7SRYvH5nMPCZnfeVx1PuFaeVHdWpra3W3lahUPDKZOT1MeRwi98VM3UZyamXZZEmXI5OZZ33KeQj1jrI0u30ZedZid/9q9/aNYMb1JTPjTvUWZqTDkcnMuD4jGRfC/NhsdLt2S+cjk5n95Kzv9p7cjJT/AT4zT7bRMEdebyZwepdk7YMd67uN3c2yXY2K3VmMdx+xy3glj11dXeEGwu/3G/6KtxPZ9vJkMvOY+PpG/8Arv+4be7qWZEvFyWTm9MivOzc1NZlaT+GFpjVdJpOZZ33KKaz8fr+l+47ltjdhdvevdm/faA3MuLZkZtyp3sKMdJhMZsb1Gcm4lbHZyHadkM6Tycx+4ut7oSc3I+VPc5EKysrKABz+mmsk5f/K7XatTxTJ6TyecMIJmDFjBoQQ4R8nUU58z9NdkIzxsbu7G/PmzUNLSwt27NiB3Nxcze13d3dj5cqVKCsr8+4PKFDCEsnppEmTAAD5+fmqPy6i9WMjZnNqdFkiu/pKZYxUfmzJKuaZEuWFjLO3oETIyHgiY3OyXjtEXnjvmAo9TEpMJitPpjL5pIj9v52ESOy8LiNGjAAAdHV1RV3/17/+FQBw6qmn2ro+JYcbsgh4K4/KL8XGDtYffvghAGDYsGG690XamEdr67e0tGD69OkAgMcffxzDhw/X3f5HH30EABg9erTucqSOObXGTE7NZpqsY54P0/rb3t3dDQCmzr8Yi3mWixk/zM6MA+wtZGLGDzObcaNjs92vHbKO2be2frr25CkxmXzppZcCACoqKsJB7+zsREVFhcyyTMnJyQFw+OTbkfvw/PPPA4jfSCS6PiVHKmQRcDaPyg/w1dTUhK9rb2/H2rVrAQAXXnhhwvuTrphH8+t3dnYiLy8Pubm5WLhwIU444YS49W3btg0AcMYZZ5jZLfoX5lS7aY69XWEmp1YyTdYxz4ep/W3v7e3F6tWrAQBFRUWW6mKe5WPGD7Mr4wr2FvIw44eZybiZsdnu1w5Zx+ybXz+te/LYE1+45ZxzZkX+WJfaRRHv/2rXay2TbGo/QgZAFBcX69ZnZX23A7x5zmQh7M2i3nLJ5lQetX6AD0D43MlanHgsvHzOZCGYR7PrK+fuNvKYKZRzGsb+4IQdUvGcyUIwp1q0ljGTUyuZtkO6nDNZCOY53vplZWW696u3f27JsxYg9c+ZLAQzHm/9RDKucLK3MCMdzpksBDMeb/3YjJsdm62+dpyQzudMFoLZN7u+F3tyM1L+B/gUlZWV4Se9rKzM1K9Oxoq83qknVvkRMmUf/H6/CAaDqj+6oFaTmfXdDvDuZLIQ9mVRb7lkczKPkT/ApzxmRk5E78Rj4fXJZCGYRzPrx/sDb+QxsVOqTiYLwZyqibd/Zpp92Y1rOk0mC8E8KyIfh+LiYtHQ0BD3fvX2zy151mJ3/2r39s1gxg9LdsbNLCNDukwmC8GMK4xk3MrYbOW144R0n0wWgtk3s74Xe3Iz0mYyWY0yOJG32N0sy2hUmEVvSoXJZDXMozel8mSyGuY0daTbZLIa5jn1pdNkshpmPPWl02SyGmY89XEyWR2zn570JpNT4pzJyq+cNzc3h6/r7e1FeXk5AODiiy+WVRqlGWaR3IR5JC9gTimVMM+U6phxSnXMOKUrZp/MyJRdQDLU1taioKAA+fn5/W7z+/245pprJFRF6YhZJDdhHskLmFNKJcwzpTpmnFIdM07pitknM1LiyGS/34+GhgaUlZWFrysuLkZlZSVWr16N7OxsidVROmEWyU2YR/IC5pRSCfNMqY4Zp1THjFO6YvbJjJQ4MhkAxo4di7Fjx2LhwoWyS6E0xyySmzCP5AXMKaUS5plSHTNOqY4Zp3TF7JNRKXFkMhERERERERERERHZi5PJRERERERERERERBQXJ5OJiIiIiIiIiIiIKC5OJv+Lz+eDz+eTXYYlSu1ql3haWlo0l+vt7UVFRQUKCgrg8/lQUFCAqqoq9Pb26m6zrq7Os49lKvNyxnt7e1FVVWU6i4B+xkker+ZRb7xV2ye17FZUVKC7u1vSHpAZXs0pcORvuLIP8+bNQ3t7e8LLkvt5ObexktmnRo7FJSUlaGlpsbN0slE6ZdxoD5HI+0FyJy8/f3Zk12wPTu6XKs+bmfmveMuanfdob2/HvHnzwo+lo+8zRYzq6mqhcnXKA+DJ/e7o6AjXrnbR09XVpbtccXGx6jb9fr/mNkOhUFIeSwCiuro6oW3oKSwsFIWFhbZt3428mvGuri7h9/s1s9jV1aW7bqL77cSYyDx6h954Gzs+9vT0WM6uFifymJOTI+bPn2/rfXiFV3MqhNDMXigUSmhZq+bPny9ycnKStr1Y6dq/qvFybiMls0/VynhlZWVSa7a7f7V7+16RDhk300Mk8n7QLLt71nTsibV4Ned2ZddMD54Iu/sJu/shL/FqxiOZmf+Kt6zZeY/I7cUu29PTk5T9a21tFQBEa2trv5t4ZHKKCAQCEEL0u+iZP3++5m0tLS1YuXIlysrK0NHRASEEOjo6UFxcjLq6OtUjlpqbm5GXl5fwvhBFWrduHerq6lBZWRmV7crKStTV1WHdunWa6+plnMgKtXFWCIFQKATg8FisqK+vR11dHYLBIHp6eiCEQE9PD8rKylBXV4fVq1fL2g1KcVVVVeHsKRltaGgAAKxcudLyskROSlafqmQ8EAiEx2Klj5g0aRI6Ozud2B2ifvQybqWHsPJ+kCjZ7MqumR6cyAlm5r+MLGtm3qO3txd5eXnw+/3hPqinpweBQAB1dXWor69PaN+M4GSyx3344YcAgHPOOcfUeuXl5fj00081b3/nnXcAADfffDNOPfVUAMCpp56K4uJiAMDWrVv7bS8/Px+VlZWm6iCKZ+bMmQCAiRMnRl2v/F+5PVa8jBMlS3d3N/Ly8hAMBjF8+PDw9c899xwAYMaMGcjOzgYAZGdno7S0FADC/xIlm5K98ePHh68bO3YsgP4TxGaWJXJKMvtUJePTp08Pj8UAcM011wAAXn755eQWT2RAvIyb6SGsvh8ksoOT2dXqwYnsZmb+y+iyZuY9PvjgAwDA5MmTw31QdnY2pk+fDuDI69BOSZ9MbmxsRElJSdR599TOSdbS0oLy8vLwcsq5QCJFnkNFObdIQUEB6urqwstUVVWFl9NbP3Y5I+dajd2fgoICNDY2JrTfsWSc+6exsRGlpaVYuHCh5jLKURpDhgyJuv6kk04CALS1tUVdX1paitra2n7BT0XMuLMZ9/v9pm83kvFUwTzKH3OXLl0Kv9+PGTNmRF1fW1urekRQ5GRGumBOnc2pkr3IrCmPT2wja2bZdMPcyhlfk92nKo9x7Nir/D/2AIl0woy7N+PsIZKHOZfTg8SyI7taPXi6YcadH8vNzH8ZXdbMvMemTZsAABdeeGHUMtnZ2RBCoLa2Nm5dCYs98UUi54ipra3VPIdNQ0ODoeUiz12mXKe2fCgUEmVlZZbXjz2njnJ9JLXtAxBlZWWW9luN1nqRFz2BQCD8eASDwfA6wWBQ9TwpO3bsiHqctO5D777j1WWk7nhg8znhrJ6Pixl3PuPKfcee17CysjK875GMZtwoN58zmXl0Po+xGhoaDN1vpNiMmuHFcyYzp3JzqvQJRjJnZlmzvHbOZOZWTm7t6FOV8w/G9sU9PT2Wxn09gHfOmcyMuzvjRtcXwvz7wUR47ZzJzLncHiRSsrNrpQc3wmvnTGbG5WfczDp6y5qZ91B6G+V25f+BQMDS7/Jo0TtnclInk5UHpqOjI3xdU1OTACCKi4v7LdfU1BS+LvLk67HLlZWVhQcTZdDQuz52fb/fH66po6MjHNDIkMWuq2wv8j56enrC60b+KI3R/bZD5Bs/tRdr5CCsnAw/8oWoFWa9kMd7sSTyRyZyG26cTGbGnc+4Umvsyej9fn+/PxRmMm6UmyeTmUc5eYzk9/tN/+BHWVmZ5R9G8OJkMnMqN6cNDQ0iEAiEx1C9SWIzy5rltclk5tb53NrVpypvwiLzHLn/yc6NVyaTmXF3Z1yLWg9h5v1gorw2mcycy++VFcnOrpUe3AivTSYz4/IzbmYcj7es0XmPyOfDznHfscnkyNnwhoaGuDvQ1dUlQqGQqK2tVW3olP/Hzqwr18duX2v9HTt2RC2nvGjUXlwKpR6toxgCgYDl/U4mtUFBCPXGWdmnyMdTK8x6IY/3AjDbFGltw42Tycy48xkXQrvRCAaDUcuZybhRbp5MZh7l5FGhNCyxR8frUWuGzPDiZDJzKjenkZSjfowcxWNmWSO8NpnM3DqfW7v6VGUCL7aHSPfJZGbc3RnXWz+2h1C2YeT9YKK8NpnMnLujB0l2dq304EZ5bTKZGZefcTPjeLxljc57RPYykc9Vssd9xyaTQ6FQ1A6rzaArtA5fVwtiLKPXm2k0tf6vd7Gy31p1GLkfs5RahDgSqtiB2uxjHO82I7cbAbhzMpkZdz7jWgNi7PVmM26UmyeTmUe5Y25xcbEA+jc8WhKdSBbCm5PJzKl7egOlGTdyJI+ZZY3w2mQyc+tsbu3uU7u6usIfkPj9/qSdCkvtvr0ymcyMuzvjsaz2EMkcx4Xw3mQycy6/B7Eju2Z7cDO8NpnMjMvPuJl19JY1Ou8RuR2tifdkjfuOTSYrQqFQ1Iy63++PGjyUZq64uFg0NDSIUCgkurq6PBtko/utV0eygqy1j0buJ/K+4n0qFHvOGq37TaR2N04mK5hx5zIebx+VgdJsxo1y82Sygnl0fsxVHj+9sTByWeWrfbGf0pvlxclkBXPqvt4gmcvG47XJZAVz60xujawbuX4ifapCOUoq8kinRAHemUxWMOPuzLgi0R4imeO4EN6bTFYw53J6ZTuya6YHt8Jrk8kKZlxen21mHTOPT+xtkRPESh+UaD3xOD6ZrOjo6NA9j0oktR/BSFaQI8+jIsSRk7/rna8qkU+7tPbbDvF+VET5GoHZBkb59CN24Fc+AYo9zD5SMvYbcPdksoIZtz/jRgdcsxk3yguTyQrm0f48Kox+vS4UCoXPj5iMH0Pw8mSygjmV1xsobxoiv2JoZtlEeHUyWcHc2ptbO/tUrYwr+5XMUwDY3b/auX1m3F0ZF8J4D2H0/WAyeHUyWcGcO9Mr25ldO09xIYR3J5MVzLhz7wcVZu5Tb1kztyl9UOzjnOxxX28yeQCSqKSkBD6fD83NzQCAU089Ff/2b/+muXx7ezsAoLe3F4FAIJmlRKmoqEBnZycAoLOzE6tXrwYAXHrppZrrFBUVAQACgQC6u7vD1zc2NsLn86G8vDx8ndn9TqbJkycDAOrr66OuV/6v7IcQQvWiiP1/Tk4OAGD16tVRj93zzz8PABg9erRNe+RuzLjzGVcet8bGRvT29oavr6qqirrdbMZTAfPofB4V27ZtAwCcccYZmst0dnYiLy8Pubm5WLhwIU444QSnynMV5lReb1BTUxO+rre3N7yPyn6YXTadMLfO5tbOPlUt4+3t7Vi7di0A4MILL7Rpr9yNGXd3xs30EEbfD6Yj5tz5HsTu7BrpwdMJMy7v/WCyGZ33AI70LhUVFVHLKq+dH//4x7bXm9Qjk5VPidQukUcIKLPoWhflKANozMwbvV75v9q5YWK/FqG2Ta1zyvj9/qhP2Izutx20flREbR/VaD2WQgjN7cb7lENvm0YB7jwymRl3PuNdXV2aWYytU02ieXTzkcnMo/N5VCifnOvlT/k6md7FLC8emcycysmpmd4gkT7CKK8dmczcyhtfI+mNlUb7VL1eOZlHJSv1euXIZGbc3Rk300Mk+n7QDK8dmcycO59zu7NrpAdPhNeOTGbG5Y/lWo+N2WXNzntoPafJ/DaKo6e5CIVCUQEoKytT/QpC5CBTVlYmduzY0e+rackKshBHfhVR68TcWtusrKwMD1hKbWoDl9H9tkNPT4+orKwMB085D44R8cIcDAbD2/X7/SIYDMb92oGZF5PeNtw4mSwEM+6GjCs/nmPkKzCJ5tHNk8lCMI8y8iiEsVzpNWxWc+nFyWQhmFNZOTXTG1jtI4zy2mSyEMytrNxGSlafGvkDfMo+JfJjqHr1emUyWQhm3M0ZN9tDJPJ+0AyvTSYLwZw7nXO7s2u1hzbKa5PJQjDjssdyM5mMt6zZeY+mpqbwYxX5A8PJojeZ7BMi+nvfNTU1mDBhQkp8Hdzn8wFASuxLuvH5fKiursb48eNt2b7yFQrlK45exYzbz4kxkXkko5zI41lnnYXx48djwYIFtt2HTMypHAsWLEBNTQ22b99uy/ZTqX9Vw9x6g939q93bl4kZ9wa7e9ZU6Ym1MOfuZ3c/YXc/JBsznlra2towcuRItLa2YsSIEVE3JfWcyURERERERERERESUmjiZTERERERERERERERxcTKZiIiIiIiIiIiIiOLKlF2AnXieFkp1zDi5CfNIXsCckhcxt5TqmHFKB8w5pTpmPH3wyGQiIiIiIiIiIiIiiouTyUREREREREREREQUFyeTiYiIiIiIiIiIiCgu100m+3w++Hw+2WWYptQdW3/s9WrLaGlpadFcrre3FxUVFSgoKIDP50NBQQGqqqrQ29ub0LJW1NXVqdZpdn/TiVcfE63ntLe3F1VVVa7II6D+2mEetXn1MVF7TvXG3Hj7qTWWJYLjo3lefUzMjI8VFRXo7u7utw1lfFS2MW/ePLS3tyetxngZNzI+M7vqvPp4JGscjcx4SUkJWlpaEqrLzGvByrLMuHlefTz0xmar463ee7RIyeorzPTZRmplxrV59TFJZg9iJGeJzHEY3Qe9bRrZL+ZcnVcfDyPPp5kxV29Zs32QW/p3vfu35fUgYlRXVwuVqx0DQOr9W6XUHVl/R0dHv+tjl9HS1dWlu1xxcbHqNv1+f0LLmhUKhTTrNLO/autWV1cnXJ+WwsJCUVhYaNv240mlnHd1dQm/36+Zsa6urqht2JlHpR61x9dqHp0YE5lHa9SeU70xVy9nemOZVXaMj07kMScnR8yfP9/W+9CTSnns6ekxNT5qLRsKhRKuz0jGjYzPVrM7f/58kZOTk9A+6GH/ak0yxlGt3FZWVlquy8xrwcyydmZcWdfO/tXu7ce771TJuBDWx9t479EUyeorzPbZRmpNJON296zsia1JRg9i9D1aInMcWsxs0+h+Wa3L7n7C7n4onlTKeCQzY268ZZPVBznVvxu5f6uvh9bWVgFAtLa29rvJdUcme50Qot8vWAYCgfD1kRc98+fP17ytpaUFK1euRFlZGTo6OiCEQEdHB4qLi1FXVxf1KYSZZc1qbm5GXl6e5u1G9pO8KfK5XbduHerq6lBZWRmV78rKStTV1WHdunXh9ezMo0LrtcM8pq7I51ZtrBVCIBQKATg8HseKN5ZZwfExfUU+t/X19airq0MwGERPTw+EEOjp6UFZWRnq6uqwevXq8HpVVVXhZZVtNDQ0AABWrlyZUE1GMm50fGZ2U5PVcVTJbSAQCGdc6QEmTZqEzs5O07WYeS2YWZYZT2+Rz2si463eezRFMvsKM3220VqZ8dRltQex8h7NyhxHPEa2aXS/mPPUpPa8mhlzjSxrpQ+S1b8bvX9bXg+x08s8ssMatbobGhoEANHQ0GBqW4FAIOrThVjBYFAAEDt27Ii6XvnUIvJIEDPLmq1RWT/ec2blOQV4ZLIbqdWtty+xt9mVR0W81068etXwyGT3Mlq3cmROMBjsd5uZscwoO8dHHpnsXmp1K+NRrJ6enn7LK8v29PTE3a4ZRvNodnw2WxePTHanRMdRrdwqGVcbd+Mx81ows6zdGVfW4ZHJ7pJobiKnYEf+AAAMzElEQVQZ6TOT3VdY7Wnt6ImF4JHJbpVoD2JmfLQ6x6HHzDbN7JcQ7uu1eWSyNVp1mxlzExmfzfZBTvXvZu/fbF22HZnc29sbPjeampKSEvh8vvB5dlpaWlBeXh4+T4dyHh49Wuf00Lq+sbExfL8FBQVobGw0tC9Wzg9nh8bGRpSWlmLhwoWayyhHeQwZMiTq+pNOOgkA0NbWZmlZM0pLS1FbW4uJEydaWt9LmHN9fr/f8O125REw9tpJBcyjeUuXLoXf78eMGTP63WbHWMbx8Yh0z2Ntba3qUQDZ2dmay0beVldXBwCorKw0fd8Ko3m0c3x2G+bWPK1xVMlobKaV/2/dutX0fZl5LZhZlhk/It0zbmW8NdpnJrsHMNNnK9gTH8acG+9BvDQ+mtkvr2PG4zMz5iYyPmv1QbL7d7vu35DY6WWzn8QoM+ax59xRZu4DgYAQQoja2trwLHjsJfKTLsTMlMf+X+/6srIy1e2XlZXF3Q+t2iIvRtZXe2xCoVD40z7869OM2E8OhBBix44dUY+HmX3Xus3MslbF246V+4HNR16Y/dSbOdeuR9nn2CN6lE/Ramtr4+5nvNviMfrasXI/bjwymXnUrzOSmSMdkjUmmtmmG/No9shk5lG/TjWxY1Ys5THVW8YKq2Oj2m1ms+u2I5OZW/06I+mNo/GOTE50vDLzWoi3rN0ZV9Zxy5HJzLh+nQojGTPTZ5q5byPM9Nlma7VSn9uOTGbO9etUo9aDmMmJ2TkOI5KxTa3eymzO3XZkMjOuX6fZZawsa/T9pIz+3ez9m3096B2ZnPBkstZXw5Q/cMpXJZSim5qawstEnmhdYTXcyhNcVlYWHnB6enrCgU/Gya/1qNUZ+WTGXvx+f9TAqJxIPvKFaOaFrXVbshsIK9uxcj+AuyaTmXP9OhsaGvqd+N3v9/cbcO3Io5nXjpX7ceNkMvOoX2ckv99v+McdkzUmmtmmG/NodjKZedSvU01ZWVm/PiBSQ0ND1FeUk9WQWh0b1W4zm123TSYzt/p1RtIbR5XHK/JxjKw/0fHKzGsh3rJ2Z1xZxy2Tycy4fp2R9enlxmyfaea+jTLaZ9vdEwvhvslk5ly/TjVqPYiZnJiZ4zAqGdvU6q3c1mub7YeYcf06zS5jZVmj7ydl9O9m79/s68HWyWQh1B9cv98viouL+y3b1dUlQqGQqK2tVW00rYZb2ZbWkRHKJzZ20WtCI1/QQqg33kr9kZ84Gd13vduS3UBY2Y4bm3ErjRBzrl2nVgMQe04hO/Jo5rVj5X7cOJksBPOoV6eiqalJAP2P2rG6PSuSPT66cTJZCOZRr85YZptu5QidZJyb0OrYqNfjGOW2yWQhmFu9OhXxxlFl8ir273+yJpMjmXktqC1rd8aVddwymSwEM65Xpxq13JjtM63etx6jfbbdPbEQ7ptMFoI516szllYPYiYnyv+NzHGYrd/qNvV6K7f12lb6IWZc3mSy2feTCqf6d7P3b3Zbtk8mK59SKJ+KKF8xiC1c67D4ZIRba7tq96EmWesbBSA8ICiDZOzgaXTf9W5LdgNhZTtW7gdw32Qyc65ep9YfebXrk51Hs68dK/fj1slk5jH+c1lcXCyA/k2P1e1Zkezx0a2TycyjsefSytEbSpNu9Ah7PVbHRrXbzGbXjZPJzG1yxtGurq7wmxa/32/6VABGmXktqC1rd8aVddw0mcyMm3seY3Njpc+0et9ajPbZTvTEQrhzMpk5T7wHSVZOjI7RZsTbZrzeymzO3TiZzIzLm0w2+35S4VT/bvb+zW7L9snk2F9sVv6YRT7gSpNZXFwsGhoaRCgUCp/nJV3CrbW8kfuO3Ha8T4Uiv9pkZlmr4u27lfAD7ptMZs7Nv9mKHcCSnUezr5149apx62Qy86j/XCr7aSZTVsaqRLfpxjxamUxmHuPnUfn6ZewvpRuRrGzqbcfs+Gy2JjdOJjO3yR9HFcpXa5N9pJKZ3MUua3fGlXXcNJnMjDv7Hi3R+za7DeBIn22lViv1uXEymTlPvAdJ1nu0ZGTe6DaN9lZma3LjZDIzLmcyOZE+yGw9dmwnGeO+3mTyACRBdnY2gsEgZs6cie7ubkyaNAnBYDDqFwVnzpwJAFixYgXGjh2L3NxcHH300Zbur7u7u991xcXFAICenh4IIVQverTWMbq+moKCgqhf11Qo/1dqNmvEiBEAgK6urqjr//rXvwIATj31VEvLkj7m3Brl10QB5jGZmEd9H330EQBg9OjRlrdBxjGP2lpaWjB9+nQAwOOPP47hw4erLqfVMyj7arVnMCodx2fmVp+RcVQrtx9++CEAYNiwYabv18xrwcyyzDgzrpA93iZTZJ+drphzbUZ7EDPjox1zHGa3aXS/UgUzLkcifZBTf09k3n9SJpMB4OKLLwYADBkyBABw1VVXqS7X3t4O4PDAEAgE4m7X7/cDAJqbm8PrLV26tN9yRUVFAIBAIBAV/sbGRvh8PpSXlxvdlaSZPHkyAKC+vj7qeuX/Ss3xXkyx/8/JyQEArF69Gp2dnQCAzs5OPP/88wCiw25mWYqPOe9P2b/GxsaoQayqqirqdiD5eTT72kk1zKO2bdu2AQDOOOMMaTWkG+axv87OTuTl5SE3NxcLFy7ECSecoLms0jPU1NSEr+vt7cXq1asBHNk/u6Rrv8DcajMyjqrltr29HWvXrgUAXHjhhabv18xrwcyyzDgzrjCaGzf0mUb7bDfUKhNz3p+ZHsTM+Gh0jsMMM9s0s1+phBl3ntU+yMn+Xer9ixiJHNavnE9E7UTgyqH4WpfYX6HUWy/yRwgiaZ0jxu/3R/0QgR3U6tH6URIYPFRebZsKre2qPfZGl9W7P6t1Wt0u4L7TXCiY8+h6urq6NDOmVpPdeYy3rtntuvU0FwrmUf25UR4XMzUkMzdG13NjHq2c5kLBPEbXo3zlUO8SyWjPYFcezfQWZmtw42kuFMyt9XFUr9eNPcermcyY6Z+TsWwyMq6s46bTXCiY8f7Po13v0YwuZ3QbZvtsO2qI5MbTXCiYc3t6kNjH08wch9GMmdmm2f0ym3M3nuZCwYzrPy9mnmsjyxp9P+nW/l3t75nZGmw/Z7JCOTF47In/FZEv/LKyMrFjxw4RCoUEcOT8L2o7V1lZGX6A9JZTllWedGV5u4OtV09PT09U/co5bBLZphBHfuxE2a7f7xfBYFD1xOBGl7Ur3Fa2C7h3Mpk5j59z5Qd4ZOQx3rpuayiEYB6tSubzbMf2jKznxjwmMpnMPKq/gdG7qNUfr2ewK49meguzNbh5Mpm5TXyyK/YxMvsDT2rM9M9Gl7Uz48o6bpxMZsbVn0c73qMZXc5Mvsz02XbVoHDzZDJznlgPYmZ8NDrHkUjO423T6H65rddOpB9ixp2dTDazPZn9u9H7t1KD3mSy718bDKupqcGECRNS+mswdvD5fADg+cfN5/MlfR+sPDY+nw/V1dUYP358UmtRKIf7K1/DJGOczrkb8ujEmMg8WsM82uOss87C+PHjsWDBAtvuIxWlQh7N3j9gfH8XLFiAmpoabN++3ZZ62L9ak265NcON/avd209Fbn6PJvv1YOWxsbtnZU9sDXOuf/+Ae3ptu/uhVOXmjJvhtddDW1sbRo4cidbW1vC51ZWbknbOZPK+5uZmBINB2WUQAWAeyV2YR3IT5pG8iLklOoKvB0oHzDnREan2euBkcpL5fL7wbL/XbNq0CTNmzEja9rz8WJA+J55b5pGMYh7JTbyYRzOY3dSU6rk1gxlPTW57XjmOkx3c9twy55RsXn5eU+31wMlkCrv77rtll0AUxjySmzCP5CbMI3kRc0t0BF8PlA6Yc6IjUu31kCm7gFTh9XO32IGPSerx8nPq5dpJnZefUy/XTurS5TlNl/1MF3w+++Njklr4fPbHxyT18Dntj49JauHzmRg7Hj8emUxEREREREREREREcXEymYiIiIiIiIiIiIji4mQyEREREREREREREcXFyWQiIiIiIiIiIiIiikvzB/iKioqcrIPIcc3Nzcw5xbVz505H7od5JCOcyuPatWvR1tbmyH1Reti+fbsj98NxlFLdo48+irVr18oug9JUc3MzLrjgAtvvg2M5yeJEr71z505mnDxhz549mrf1m0w+5ZRTUFhYaGtBRPEUFhbilFNOsW37+fn5tm2bUsvJJ59s+5jIPJJRTuTx6quvxieffGLrfVD6Oeuss2z9u87+ldzA7v6VGSfZLrjgAlv7VvbEJJvdvfaIESNw1VVX2bZ9omQ65phjUFhYiOzs7H63+YQQQkJNREREREREREREROQdbTxnMhERERERERERERHFxclkIiIiIiIiIiIiIoqLk8lEREREREREREREFNf/BwTc5dmY0mw2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入 StringIO 模块用于创建内存中的文件\n",
    "from six import StringIO\n",
    "\n",
    "# 从 IPython.display 模块中导入 Image 类\n",
    "from IPython.display import Image  \n",
    "\n",
    "# 导入 pydot 模块\n",
    "import pydot\n",
    "from sklearn import tree\n",
    "import os\n",
    "\n",
    "# 创建一个 StringIO 对象，用于将决策树导出为Graphviz格式的数据\n",
    "dot_data = StringIO()\n",
    "\n",
    "# 使用 tree.export_graphviz 将决策树导出为Graphviz格式的数据，并保存到 dot_data 中\n",
    "tree.export_graphviz(clf, out_file=dot_data) \n",
    "\n",
    "# 从 dot_data 中读取Graphviz格式的数据，创建图形对象列表\n",
    "graphs = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "\n",
    "# 选择第一个图形对象\n",
    "graph = graphs[0]\n",
    "\n",
    "# 指定Graphviz的可执行文件路径，替换为您的实际路径\n",
    "graph.set_prog(\"C:/Program Files/Graphviz/bin/dot.exe\")\n",
    "\n",
    "# 使用 Image 类将图形对象显示为PNG格式图像\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "细微的差别，但规则是一样的，很酷!"
   ]
  },
  {
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